CDNs are confronted with a sharp increase in traffic related to live video (channel) streaming. Previous theoretical models that deal with streaming capacity problems do not capture the emerging reality faced by today’s CDNs, in particular rate-adaptive streaming. In this presentation, we identify a new, discretized streaming model for live video delivery in CDNs. For this model we formulate a general optimization problem. Then we study a practical scenario that occurs in real CDNs. We present a fast, easy to implement, and near-optimal algorithm with performance approximation ratios that are negligible for large network. More details in: http://enstb.org/~gsimon/Resources/algotel13.pdf http://enstb.org/~gsimon/Resources/icccn13.pdf

1. Fast Near-Optimal Algorithm
for Delivering Multiple Live
Video Channels in CDNs
Jiayi Liu and Gwendal Simon
Telecom Bretagne
28/05/2013

2. Context : live stream delivery in CDN
Content Provider
encoders
ingest
server
CDN provider
origin
server
edge
servers
Clients
Content provider : content generation
CDN provider : content delivery
Clients : content consumption
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3. Context : live stream delivery in CDN
3-tier CDN topology (Akamai CDN delivery network)
sources
reflectors
edge servers
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4. Context : live stream delivery in CDN
3-tier CDN topology (Akamai CDN delivery network)
sources
reflectors
edge servers
Phase 1 : Sources transcode streams
Phase 2 : Reflectors deliver streams
Phase 3 : Edge servers offer streams to end users
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5. Current trend
Diverse user devices
video service
ADSL/FTTH 3G
WiFi
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6. Current trend
Rate adaptive streaming (DASH standard)
video
representation 1
representation 2
...
representation n
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7. Current trend
Rate adaptive streaming (DASH standard)
video
representation 1
representation 2
...
representation n
bitrate
150 kbps
240 kbps
...
4540 kbps
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8. Current trend
Rate adaptive streaming (DASH standard)
video
representation 1
representation 2
...
representation n
bitrate
150 kbps
240 kbps
...
4540 kbps
quality
low
high
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9. Current trend
Rate adaptive streaming (DASH standard)
video service
ADSL/FTTH 3G
WiFi
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10. Current trend
Rate adaptive streaming (DASH standard)
video service
ADSL/FTTH 3G
WiFi
Req_repHD Req_replow
Req_repmedium
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11. Challenges
DASH high aggregated video bit-rate
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12. Challenges
DASH high aggregated video bit-rate
Netflix has 14 representations with 15 Mbps/video
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13. Challenges
DASH high aggregated video bit-rate
Netflix has 14 representations with 15 Mbps/video
Heavy transmission burden on CDN
CDN can be underprovisioned
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14. Challenges
DASH high aggregated video bit-rate
Netflix has 14 representations with 15 Mbps/video
Heavy transmission burden on CDN
CDN can be underprovisioned
→ Challenges :
live DASH streaming in under-provisioned CDN
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15. Outline
1. Discretized streaming capacity problem
2. A practical scenario and an algorithm
3. Evaluation
4. Conclusion
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16. Avancement
1 Discretized streaming capacity problem
2 A practical scenario and an algorithm
3 Evaluation
4 Conclusion
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17. Discretized streaming capacity problem
Goal : maximize the throughput of CDN
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18. Discretized streaming capacity problem
Goal : maximize the throughput of CDN
previous work : streaming capacity problem
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19. Discretized streaming capacity problem
Goal : maximize the throughput of CDN
previous work : streaming capacity problem
maximizing deliverable bit-rate in P2P network
elastic video bit-rate based
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20. Discretized streaming capacity problem
Goal : maximize the throughput of CDN
previous work : streaming capacity problem
maximizing deliverable bit-rate in P2P network
elastic video bit-rate based
our work : discretized streaming capacity problem
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21. Discretized streaming capacity problem
Goal : maximize the throughput of CDN
previous work : streaming capacity problem
maximizing deliverable bit-rate in P2P network
elastic video bit-rate based
our work : discretized streaming capacity problem
DASH : stream bit-rate predefined
throughput : the number delivered streams
stream utility : gain of edge server for stream
maximizing the utility of delivered streams
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22. Problem formulation
Objective : max i,j,e αi,j
e · xi,j
e
di,j : i-th representation of the j-th channel
xi,j
e : indicates if edge server e receives di,j
αi,j
e : utility of edge server e on di,j
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23. Problem formulation
Objective : max i,j,e αi,j
e · xi,j
e
di,j : i-th representation of the j-th channel
xi,j
e : indicates if edge server e receives di,j
αi,j
e : utility of edge server e on di,j
Problem definition
Delivery trees : Tij
Problem : Given the topology and capacity
constraints of a CDN, find delivery tree sets, {Tij},
such that i,j,e αi,j
e · xi,j
e is maximized.
ILP formulation and NP-complete complexity 1
1. Jiayi Liu and Gwendal Simon, Fast Near-Optimal Algorithm for Delive-
ring Multiple Live Video Channels in CDNs, ICCCN, 2013.
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24. Avancement
1 Discretized streaming capacity problem
2 A practical scenario and an algorithm
3 Evaluation
4 Conclusion
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25. A practical scenario
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26. A practical scenario
CDN full connectivity
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27. A practical scenario
CDN full connectivity
Homogeneous CDN equipments capacity C
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28. Bottom-up tree construction
One tree per stream ; one tree per reflector
border
reflectors
edge servers
intermediate
reflectors
source
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29. Bottom-up tree construction
One tree per stream ; one tree per reflector
border
reflectors
edge servers
intermediate
reflectors
source
To deliver di (with bit rate λi) to gi edge servers :
Number of streams a node can forward : δi = C/λi
Number of border reflectors : mi = gi /δi
Number of intermediate reflectors : mi −1
δi −1
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30. Greedy Algorithm
utility score per rate unit (uspru) : αi
e
λi
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31. Greedy Algorithm
utility score per rate unit (uspru) : αi
e
λi
Iterate on uspru in decreasing order
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32. Greedy Algorithm
utility score per rate unit (uspru) : αi
e
λi
Iterate on uspru in decreasing order
In each iteration :
A uspru with a certain edge server and stream
Estimate the number of reflectors needed
If the CDN can afford, continue ; else end.
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33. Greedy Algorithm
utility score per rate unit (uspru) : αi
e
λi
Iterate on uspru in decreasing order
In each iteration :
A uspru with a certain edge server and stream
Estimate the number of reflectors needed
If the CDN can afford, continue ; else end.
Results : A set of edge servers, and number of
reflectors used in each tree
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34. Analysis : approximate ratio
Wasted bandwidth for each tree :
border
reflectors
edge servers
intermediate
reflectors
source
Unused border reflector
capacity
Intermediate reflector
capacity
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35. Analysis : approximate ratio
Unused border reflectors bandwidth =
total bandwidth (mi C) - used bandwidth
border
reflectors
edge servers
intermediate
reflectors
source
Used bandwidth ≥ (mi − 1)δi λi
C ≤ (δi + 1)λi
Unused border reflector bandwidth ≤ mi λi + C
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36. Analysis : approximate ratio
Capacity of intermediate reflectors :
border
reflectors
edge servers
intermediate
reflectors
source
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37. Analysis : approximate ratio
Capacity of intermediate reflectors :
border
reflectors
edge servers
intermediate
reflectors
source
• Connect to borders re-
flectors : mi λi
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38. Analysis : approximate ratio
Capacity of intermediate reflectors :
border
reflectors
edge servers
intermediate
reflectors
source
• Connect to borders re-
flectors : mi λi
• Inter-intermediate reflec-
tors connection : ≤ mi λi
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39. Analysis : approximate ratio
Capacity of intermediate reflectors :
border
reflectors
edge servers
intermediate
reflectors
source
• Connect to borders re-
flectors : mi λi
• Inter-intermediate reflec-
tors connection : ≤ mi λi
• Unused : ≤ C
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40. Analysis : approximate ratio
Capacity of intermediate reflectors :
border
reflectors
edge servers
intermediate
reflectors
source
• Connect to borders re-
flectors : mi λi
• Inter-intermediate reflec-
tors connection : ≤ mi λi
• Unused : ≤ C
• Finally, ≤ 2mi λi + C
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41. Analysis : approximate ratio
Wasted bandwidth for each tree ≤ 3miλi + 2C
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42. Analysis : approximate ratio
Wasted bandwidth for each tree ≤ 3miλi + 2C
Wasted bandwidth for all trees ≤ 3Nr λ∗
+ 2NchNrpC
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43. Analysis : approximate ratio
Wasted bandwidth for each tree ≤ 3miλi + 2C
Wasted bandwidth for all trees ≤ 3Nr λ∗
+ 2NchNrpC
Finally, S ≥ wasted
Nr C S∗
≥ Nr C−3Nr λ∗1
−2NchNrpC
Nr C S∗
= 1 − 3λ∗
C − 2NchNrp
Nr
S∗
1. λ∗
= maxi λi
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44. Avancement
1 Discretized streaming capacity problem
2 A practical scenario and an algorithm
3 Evaluation
4 Conclusion
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45. Setting
3 sources
20 to 100,000 reflectors
CDN network provisioning 70%
3 channels with 5 representations each
C = 200 Mbps
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46. Evaluation
S∗
calculated based on a theoretical upper bound
Running time : less than 30 seconds
Approximate ratio : 0.978 for 200 reflectors ; 0.993 for 1000
reflectors
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47. Avancement
1 Discretized streaming capacity problem
2 A practical scenario and an algorithm
3 Evaluation
4 Conclusion
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48. Conclusion
Discretized streaming model for live DASH
streaming
ILP formulation and NP-Completeness
A fast and near-optimum algorithm
Future work
Define specific utility
Distributed algorithm
Live DASH streaming CDN system
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