Towards View-Aware Adaptive Streaming of Holographic Content
•Download as PPTX, PDF•
0 likes•1,320 views
Holography is able to reconstruct a three dimensional structure of an object by recording full wave fields of light emitted from the object. This requires a huge amount of data to be encoded, stored, transmitted, and decoded for holographic content, making it a practical usage challenging, specifically for bandwidth-constrained networks and memory-limited devices. In the delivery of holographic content via the internet, bandwidth wastage should be avoided to tackle high bandwidth demands of holography streaming. For real-time applications, encoding time-complexity is also a major problem. In this paper, the concept of Dynamic Adaptive Streaming over HTTP (DASH) is extended to holography image streaming and view-aware adaptation techniques are studied. As each area of a hologram contains information of a specific view and instead of encoding and decoding the entire hologram, just the part required to render the selected view is encoded and transmitted via the network based on the users’ interactivity. Four different strategies, namely, (i) monolithic, (ii) single view, (iii) adaptive view, and (iv) non-real time streaming are explained and compared in terms of (a) bandwidth requirements, (b) encoding time-complexity, and (c) bitrate overhead. Experimental results show that the view-aware methods reduce the required bandwidth for holography streaming at the cost of a bitrate increase.
Report
Share
Report
Share
Recommended
HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
Recorded cataract surgery videos play a prominent role in training and investigating the surgery, and enhancing the surgical outcomes. Due to storage limitations in hospitals, however, the recorded cataract surgeries are deleted after a short time and this precious source of information cannot be fully utilized. Lowering the quality to reduce the required storage space is not advisable since the degraded visual quality results in the loss of relevant information that limits the usage of these videos. To address this problem, we propose a relevance-based compression technique consisting of two modules: (i) relevance detection, which uses neural networks for semantic segmentation and classification of the videos to detect relevant spatio-temporal information, and (ii) content-adaptive compression, which restricts the amount of distortion applied to the relevant content while allocating less bitrate to irrelevant content. The proposed relevance-based compression framework is implemented considering five scenarios based on the definition of relevant information from the target audience’s perspective. Experimental results demonstrate the capability of the proposed approach in relevance detection. We further show that the proposed approach can achieve high compression efficiency by abstracting substantial redundant information while retaining the high quality of the relevant content.Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Ne...
Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Ne...Alpen-Adria-Universität
HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5% while increasing the difference in video multimethod assessment fusion to 5.6 points.FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...Alpen-Adria-Universität
Recommended
HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
Recorded cataract surgery videos play a prominent role in training and investigating the surgery, and enhancing the surgical outcomes. Due to storage limitations in hospitals, however, the recorded cataract surgeries are deleted after a short time and this precious source of information cannot be fully utilized. Lowering the quality to reduce the required storage space is not advisable since the degraded visual quality results in the loss of relevant information that limits the usage of these videos. To address this problem, we propose a relevance-based compression technique consisting of two modules: (i) relevance detection, which uses neural networks for semantic segmentation and classification of the videos to detect relevant spatio-temporal information, and (ii) content-adaptive compression, which restricts the amount of distortion applied to the relevant content while allocating less bitrate to irrelevant content. The proposed relevance-based compression framework is implemented considering five scenarios based on the definition of relevant information from the target audience’s perspective. Experimental results demonstrate the capability of the proposed approach in relevance detection. We further show that the proposed approach can achieve high compression efficiency by abstracting substantial redundant information while retaining the high quality of the relevant content.Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Ne...
Relevance-Based Compression of Cataract Surgery Videos Using Convolutional Ne...Alpen-Adria-Universität
HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5% while increasing the difference in video multimethod assessment fusion to 5.6 points.FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...Alpen-Adria-Universität
HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...Alpen-Adria-Universität
Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA(joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers(ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance ofQuaLAin achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutionsA Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...Alpen-Adria-Universität
Omnidirectional video (ODV) streaming applica- tions are becoming increasingly popular. They enable a highly immersive experience as the user can freely choose her/his field of view within the 360-degree environment. Current deployments are fairly simple but viewport-agnostic which inevitably results in high storage/bandwidth requirements and low Quality of Experience (QoE). A promising solution is referred to as tile- based streaming which allows to have higher quality within the user’s viewport while quality outside the user’s viewport could be lower. However, empirical QoE assessment studies in this domain are still rare. Thus, this paper investigates the impact of different tile-based streaming approaches and configurations on the QoE of ODV. We present the results of a lab-based subjective evaluation in which participants evaluated 8K omnidirectional video QoE as influenced by different (i) tile-based streaming approaches (full vs. partial delivery), (ii) content types (static vs. moving camera), and (iii) tile encoding quality levels determined by different quantization parameters. Our experimental setup is character- ized by high reproducibility since relevant media delivery aspects (including the user’s head movements and dynamic tile quality adaptation) are already rendered into the respective processed video sequences. Additionally, we performed a complementary objective evaluation of the different test sequences focusing on bandwidth efficiency and objective quality metrics. The results are presented in this paper and discussed in detail which confirm that tile-based streaming of ODV improves visual quality while reducing bandwidth requirements.Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...Alpen-Adria-Universität
More Related Content
What's hot
HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...Alpen-Adria-Universität
Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA(joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers(ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance ofQuaLAin achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutionsA Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...Alpen-Adria-Universität
Omnidirectional video (ODV) streaming applica- tions are becoming increasingly popular. They enable a highly immersive experience as the user can freely choose her/his field of view within the 360-degree environment. Current deployments are fairly simple but viewport-agnostic which inevitably results in high storage/bandwidth requirements and low Quality of Experience (QoE). A promising solution is referred to as tile- based streaming which allows to have higher quality within the user’s viewport while quality outside the user’s viewport could be lower. However, empirical QoE assessment studies in this domain are still rare. Thus, this paper investigates the impact of different tile-based streaming approaches and configurations on the QoE of ODV. We present the results of a lab-based subjective evaluation in which participants evaluated 8K omnidirectional video QoE as influenced by different (i) tile-based streaming approaches (full vs. partial delivery), (ii) content types (static vs. moving camera), and (iii) tile encoding quality levels determined by different quantization parameters. Our experimental setup is character- ized by high reproducibility since relevant media delivery aspects (including the user’s head movements and dynamic tile quality adaptation) are already rendered into the respective processed video sequences. Additionally, we performed a complementary objective evaluation of the different test sequences focusing on bandwidth efficiency and objective quality metrics. The results are presented in this paper and discussed in detail which confirm that tile-based streaming of ODV improves visual quality while reducing bandwidth requirements.Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...Alpen-Adria-Universität
What's hot (20)
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR Systems
Towards Optimal Multirate Encoding for HTTP Adaptive Streaming
Towards Optimal Multirate Encoding for HTTP Adaptive Streaming
A Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...
On the Impact of Viewing Distance on Perceived Video Quality
On the Impact of Viewing Distance on Perceived Video Quality
Video complexity analyzer (VCA) for streaming applications
Video complexity analyzer (VCA) for streaming applications
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective Qo...
MHV'22 - Take the Red Pill for H3 and See How Deep the Rabbit Hole Goes
MHV'22 - Take the Red Pill for H3 and See How Deep the Rabbit Hole Goes
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Timing verification of automotive communication architectures using quantile ...
Timing verification of automotive communication architectures using quantile ...
Tutorial on Point Cloud Compression and standardisation
Tutorial on Point Cloud Compression and standardisation
Similar to Towards View-Aware Adaptive Streaming of Holographic Content
Similar to Towards View-Aware Adaptive Streaming of Holographic Content (20)
Immersive Video Delivery: From Omnidirectional Video to Holography
Immersive Video Delivery: From Omnidirectional Video to Holography
A Hybrid DWT-SVD Method for Digital Video Watermarking Using Random Frame Sel...
A Hybrid DWT-SVD Method for Digital Video Watermarking Using Random Frame Sel...
Robust Video Watermarking Scheme Based on Intra-Coding Process in MPEG-2 Style
Robust Video Watermarking Scheme Based on Intra-Coding Process in MPEG-2 Style
Fixation Prediction for 360° Video Streaming in Head-Mounted Virtual Reality
Fixation Prediction for 360° Video Streaming in Head-Mounted Virtual Reality
(Slides) P2P video broadcast based on per-peer transcoding and its evaluatio...
(Slides) P2P video broadcast based on per-peer transcoding and its evaluatio...
International journal of signal and image processing issues vol 2015 - no 1...
International journal of signal and image processing issues vol 2015 - no 1...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Evaluation of bandwidth performance for interactive spherical video
Evaluation of bandwidth performance for interactive spherical video
Video Compression Algorithm Based on Frame Difference Approaches
Video Compression Algorithm Based on Frame Difference Approaches
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
More from Alpen-Adria-Universität
In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70%, a reduction in the total number of CPU threads used by 63.83%, and a 37.87% reduction in the overall encoding time, considering a JND of six VMAF points.Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Alpen-Adria-Universität
Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Alpen-Adria-Universität
Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...Alpen-Adria-Universität
HTTP Adaptive Streaming (HAS) methods divide a video into smaller segments, encoded at multiple pre-defined bitrates to construct a bitrate ladder. Bitrate ladders are usually optimized per title over several dimensions, such as bitrate, resolution, and framerate. This paper adds a new dimension to the bitrate ladder by considering the energy consumption of the encoding process. Video encoders often have multiple pre-defined presets to balance the trade-off between encoding time, energy consumption, and compression efficiency. Faster presets disable certain coding tools defined by the codec to reduce the encoding time at the cost of reduced compression efficiency. Firstly, this paper evaluates the energy consumption and compression efficiency of different x265 presets for 500 video sequences. Secondly, optimized presets are selected for various representations in a bitrate ladder based on the results to guarantee a minimal drop in video quality while saving energy. Finally, a new per-title model, which optimizes the trade-off between compression efficiency and energy consumption, is proposed. The experimental results show that decreasing the VMAF score by 0.15 and 0.39 while choosing an optimized preset results in encoding energy savings of 70% and 83%, respectively.Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...Alpen-Adria-Universität
With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission.
To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., HEVC, AV1) that lie below the predicted rate-distortion curve of the AVC codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Alpen-Adria-Universität
This paper presents UtilML, a novel approach for tackling resource utilization prediction challenges in the computing continuum. UtilML leverages Long-Short-Term Memory (LSTM) neural networks, a machine learning technique, to forecast resource utilization accurately. The effectiveness of UtilML is demonstrated through its evaluation of data extracted from a real GPU cluster in a computing continuum infrastructure comprising more than 1800 computing devices. To assess the performance of UtilML, we compared it with two related approaches that utilize a Baseline-LSTM model. Furthermore, we analyzed the LSTM results against User-Predicted values provided by GPU cluster owners for task deployment with estimated allocation values. The results indicate that UtilML outperformed user predictions by 2% to 27% for CPU utilization prediction. For memory prediction, UtilML variants excelled, showing improvements of 17% to 20% compared to user predictions.Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Alpen-Adria-Universität
Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low- latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client- based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Alpen-Adria-Universität
The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today’s internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...Alpen-Adria-Universität
The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to measure energy consumption for each component accurately and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming [1, 2, 3]. These components are classified into three categories [4]: (i) data centers, which include encoding, packaging, and storage on cloud data centers; (ii) networks, which include core network and access networks; and (iii) end-user devices which involve decoding, players, hardware, etc.
In addition to identifying the primary components of video streaming that affect energy consumption, it is important to conduct a comprehensive analysis of the entire video streaming. It is also essential to balance energy optimization and service quality to ensure that energyefficient strategies are implemented without sacrificing the quality of video streaming services.
This talk aims to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesAlpen-Adria-Universität
The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices.
In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Alpen-Adria-Universität
Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times. In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics. In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE. This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution. Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS). We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players. Our testbed generates the test content (audiovisual streams). Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment. We have integrated the ITU-T P.1203 quality model inside our testbed. To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...Alpen-Adria-Universität
More from Alpen-Adria-Universität (20)
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video Processing
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission Prediction
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive Streaming
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
LLL-CAdViSE: Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation...
Recently uploaded
💉💊+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHABI}}+971581248768
+971581248768 Mtp-Kit (500MG) Prices » Dubai [(+971581248768**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Maya Whatsapp +971581248768 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971581248768''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971581248768' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Cl+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
A Principled Technologies deployment guide
Conclusion
Deploying VMware Cloud Foundation 5.1 on next gen Dell PowerEdge servers brings together critical virtualization capabilities and high-performing hardware infrastructure. Relying on our hands-on experience, this deployment guide offers a comprehensive roadmap that can guide your organization through the seamless integration of advanced VMware cloud solutions with the performance and reliability of Dell PowerEdge servers. In addition to the deployment efficiency, the Cloud Foundation 5.1 and PowerEdge solution delivered strong performance while running a MySQL database workload. By leveraging VMware Cloud Foundation 5.1 and PowerEdge servers, you could help your organization embrace cloud computing with confidence, potentially unlocking a new level of agility, scalability, and efficiency in your data center operations.Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Principled Technologies
Recently uploaded (20)
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Towards View-Aware Adaptive Streaming of Holographic Content
- 1. Towards View-Aware Adaptive Streaming of Holographic Content Hadi Amipour1, Christian Timmerer1,2, and Mohammad Ghanbari1,3 1Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria 2Bitmovin, Klagenfurt, Austria 3School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK This research has been supported in part by the Christian Doppler Laboratory ATHENA: https://athena.itec.aau.at/
- 2. Challenges for digital holographic video display systems 2 01 Displays Very premature and heterogeneouse in design No established standard how to supply holographic data to the display 02 Recording Recording at high resolution is difficult Require expertise to build and operate 03 CGH More calculation-intensive than classical image rendering 04 Coding New transform is needed for digital holograms 05 QoE Accurate model is required for modelling perceptual visual quality
- 3. Workflow for end-to-end hologram delivery 3Signal processing challenges for digital holographic video display systems
- 4. 4 Dataset The dataset consists of diffuse holograms generated from 3D point clouds Interfere-II Resolution: 8192 x 8192 Pixel pitch: 1 um Wavelength: 633 nm Field of view: 370° Full parallax
- 5. 5 Compression Each hologram is stored as a matrix that contains complex numbers. In order to encode each hologram in the hologram plane, each raw hologram is divided into two parts, real and imaginary. Both real and imaginary parts are encoded using an ordinary image/video encoder.
- 6. 6 Viewports Each hologram contains information of all views of an object To render each requested view, the corresponding area of that view in the hologram is extracted
- 7. Adaptive holography streaming 7 01 Monolithic streaming 02 Single view streaming 03 Adaptive view streaming 04 Non-real time streaming
- 8. 8 Monilithic streaming The entire hologram is sent. The delivery of out of viewport areas of a holographic content leads to bandwidth wastage Increased encoding/decoding time-complexity Best user interactivity streaming HTTPSeg1Seg1 Highest bitrateLowest bitrate HTTP Server Client Select viewDecoder
- 9. 9 Single view streaming One view is requested and the corresponding segment is transmitted Highest possible bandwidth reduction Reduces encoding/decoding time-complexity It is impractical in user interactive hologram
- 10. 10 Single view streaming One view is requested and the corresponding segment is transmitted Highest possible bandwidth reduction Reduces encoding/decoding time-complexity It is impractical in user interactive hologram HTTPSeg_1Seg_1 Highest bitrateLowest bitrate HTTP Server Client Select view Decoder Seg_2 Seg_N Seg_2 Seg_N
- 11. 11 Adaptive view streaming Each partition of holograms is extended to a larger partition Increases the user experience HTTPSeg_1Seg_1 Highest bitrateLowest bitrate HTTP Server Client Select view Decoder Seg_2 Seg_N Seg_2 Seg_N d d = 256 512x512 views
- 12. 12 Adaptive view streaming Each partition of holograms is extended to a larger partition Increases the user experience d d = 256 512x512 views
- 13. 13 Non-real time streaming For each hologram 8192x8192 single views should be stored 2048 1024
- 14. 14 Bandwidth Bandwidth requirements for various streaming strategies
- 15. 15 Bandwidth Bandwidth requirements for various streaming strategies
- 16. 16 Bandwidth Bandwidth requirements for various streaming strategies
- 17. 17 Time Complexity Encoding time-complexity for various streaming strategies Results divided to max value
- 18. Conclusion 18 01 Monolithic streaming The entire hologram is encoded and transmitted Requires the highest bandwidth and encoding/decoding time complexity All views are available in the client side 02 Single view streaming Only one view is transmitted Requires the lowest bandwidth and encoding/decoding time complexity Impractical in user interactive display systems 03 Adaptive view streaming In addition to the requested viewport, its neighboring views are transmitted Efficient in terms of bandwidth consumption A trade-off is established between user interactivity and bandwidth consumption 04 Non-real time streaming The overall bitrate increases compared to the monolithic streaming The storage space is decreased compared to the single/adaptive view streaming