Live game streaming platforms such as Twitch allow gamers to broadcast their gameplay over the Internet. The popularity of these platforms boosts the market of eSport but poses new delivery problems. In this paper, we focus on the implementation of adaptive bitrate streaming in massive live game streaming platforms. Based on three months of real data traces from Twitch, we motivate the need for an adoption of adaptive bitrate streaming in this platform to reduce the delivery bandwidth cost and to increase QoE of view- ers. We show however that a naive implementation requires the reservation of a large amount of computing resources for transcoding purposes. To address the trade-off between benefits and costs, we formulate a management problem and we design two strategies for deciding which online channels should be delivered by adaptive bitrate streaming. Our evaluations based on real traces show that these strategies can reduce the overall infrastructure cost by 40% in comparison to an implementation without adaptive streaming.

1. DASH in Twitch:
Adaptive Bitrate
Streaming in Live Game
Streaming Platforms
Karine Pires, Gwendal Simon
Telecom Bretagne, France
Nov 2014

2. Live Streaming : A New Context
Cloud Delivery Process
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3. Live Streaming : A New Context
Cloud Delivery Process
more and more
non-professional
broadcasters
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4. Live Streaming : A New Context
Cloud Delivery Process
more and more CDN
heterogeneous
end-viewers
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5. Live Streaming : A New Context
Cloud Delivery Process
CDN
Data-Center
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6. Live Streaming : A New Context
Cloud Delivery Process
CDN
Data-Center
Adaptive Streaming
1 video stream = k representations
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7. Live Streaming : A New Context
Cloud Delivery Process
CDN
Data-Center
4th in US internet traffic
+55 millions unique visitors
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8. Two options for the delivery process
Traditional Process
Raw
Video
Ingest
Servers
Delivery
Servers
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9. Two options for the delivery process
Traditional Process
Raw
Video
Ingest
Servers
Delivery
Servers
Transcoding Process
Raw
Ingest
Video
Servers
Transcode
Servers
Delivery
Servers
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10. Which live streams to transcode
Traditional
Process
# CPU
# QoE
" BW
Transcoding
Process
" CPU
" QoE
# BW
Live stream ?
Trade-off
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11. Our contributions
Adaptive Bit-Rate streaming in Twitch :
Real traces from Twitch
Decision of which channels should be delivered with ABR
Comparison of different decision strategies
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12. A Tour on Twitch
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13. What is a broadcaster
A broadcaster streaming two gaming sessions
nb. of viewers
online online
time
t1 t01
t2 t02
session 1 session 2
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14. Data retrieval
For 3 months ! from 6 Jan. to 6 Apr.
time
all
channels
of one
snapshot
9 :00 9 :05 9 :10 9 :15 9 :20
every five minutes ! one snapshot
Dataset available : http ://dash.ipv6.enstb.fr/dataset/twitch/
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15. How many online broadcasters
0 10 20 30 40 50 60 70 80 90
10K
8K
6K
4K
2K
0
Days
Nb. of online channels
min max
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16. How much bandwidth is needed
0 10 20 30 40 50 60 70 80 90
2
1
0
Days
Bandwidth (Tbps)
min max
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17. Raw videos properties
240p 360p 480p 720p 1080p
0.6
0.4
0.2
0
Representation
Sessions ratio
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18. Raw videos properties
240p 360p 480p 720p 1080p
0.6
0.4
0.2
0
Representation
Sessions ratio
1
0.8
0.6
0.4
0.2
0
0.1 1 2 10
Video bit-rate (Mbps)
CDF of the sessions
480p
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19. Raw videos properties
240p 360p 480p 720p 1080p
0.6
0.4
0.2
0
Representation
Sessions ratio
1
0.8
0.6
0.4
0.2
0
0.1 1 2 10
Video bit-rate (Mbps)
CDF of the sessions
480p 720p 1080p
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20. Selecting the ABR
streams
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21. When to decide a stream should be transcoded
At-startup
Strategy
transcoding decision
time
t1 t01
On-the-fly
Strategy
transcoding decision
time
t1 t01
transcoding traditional
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22. Strategies
None : no ABR
All : all streams are streamed with ABR
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23. Strategies
None : no ABR
All : all streams are streamed with ABR
Top-50 (at-startup) : streams that were popular
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24. Strategies
None : no ABR
All : all streams are streamed with ABR
Top-50 (at-startup) : streams that were popular
Threshold-1000 (on-the-fly) immediate popularity
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25. Evaluation settings
Sessions ! Twitch dataset
Viewers ! DASH dataset
ABR ! proportional to raw video
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26. Results : degraded viewers served
0 10 20
1
0.5
0
All
None
Days
Viewers ratio
Top Threshold
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27. Results : costs evaluation
None All Top Threshold
150
100
50
0
-39% -40%
+5%
Strategies
Total Price ($M)
Delivery Transcode
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28. Conclusion
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29. Discussions
preliminary work=global picture!details neglected
more formal and accurate formulation
more precise QoE estimation
better transcoding needs model
management of different hardware resources
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30. Discussions
preliminary work=global picture!details neglected
more formal and accurate formulation
more precise QoE estimation
better transcoding needs model
management of different hardware resources
more efficient strategies can be designed
sophisticated strategies
statistical approaches for popularity forecasting
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