Authors: Taehyun Kim and Mostafa H. Ammar Presented by: Koushik Ananthasayanam Varun Kulkarni

1. Paper #09 11- 10 – 2009A Comparison of Heterogeneous Video Multicast schemes: Layered encoding or Stream Replication Authors: Taehyun Kim and Mostafa H. Ammar Presented by: KoushikAnanthasayanam VarunKulkarni

2. Overview ~ • Aim of the research paper • Comparison between Replication and Layering • Experiments based on the Comparisons • Results • Conclusion

3. What this paper aims at ? • A structured and systematic comparison of video multicasting schemes. • Only those schemes that deal with the heterogeneous receivers. • Replicated Streams. • Cumulative layering. • Non- cumulative Layering.

4. Aim (Contd.) • ‘Layered multicast transmission is superior to the replicated stream multicasting’ – widely believed. • Authors contradict this dogma – bandwidth overhead which is incurred by encoding video stream in layers, cannot be neglected while comparison.

5. Replicated Streams ~ • More than one video streams. • Replicated – same contents but with different data rates. • However, receiver subscribes to only one suitable stream. • Examples: SureStream by RealNetworks. Intelligent Streaming by Microsoft.

6. Replicated Streams (Contd.)

7. Replicated Streams (Contd.) • R1, R2 and R3 are from different domain • Receivers subscribe to only one stream • R1 joins the high quality stream (8.5Mbps) • R2 receives the medium quality stream (1.37Mbps) • R3 joins the low quality stream (128kbps)

8. Cumulative Layering ~ • Video can encoded in a base layer and one or more enhancement layers. • Base Layer: Independently decoded. • Enhancement Layer(s): Decoded with lower layers to improve the video quality. Layer ‘k’ can be only be decoded along with layers 1 to k-1. • Example: MPEG-2 scalability modes.

9. Non- Cumulative Layering ~ • Video is encoded in two or more independent layers. • Two or more independently decoded layers. • Receivers select any subset of video layer and join it, without joining the layer-1 multicast group. • Eg: Multiple Description Coding.

10. Layered Multicast (Contd.)

11. Layered Multicast (Contd.) • R1 subscribes to all video layers (10 Mbps) • R2 joins enhancement layers 1 and the base layer (1.5 Mbps) • R3 just receives the base layer (128kbps)

12. Layering or Replication ? • Common belief: ‘Layering is better than replication.’ - Really ? • Bandwidth overhead in layering. • Cater to specifics of encoding. • Implicit Protocol Complexity • Topological placement of receivers

13. Layering or Replication ? (Contd.)

14. Layering or Replication (Contd.) • Assuming 20% overhead, the data rates contributing to the video quality are 8Mbps, 1.2Mbps and 102.4Kbps • Stream Replication: video quality are 8.5Mbps, 1.37Mbps and 128kbps

15. Overhead in Layered Video ~ Information theoretic results: • Performance of layered coding is not better than that of non-layered coding. Increase the number of layers - significant quality degradation. Packetization Overhead: • Enhancement layers carry: Picture header, GoP information and Macroblock information. Protocol Overhead: • Receivers need to manage the multiple subscriptions in layered video.

16. Experimental Evidence ~ • Non-layered streams has better video quality • The layering overhead ranges from 0.4% at 27.7dB PSNR to 117% at 23.2dB PSNR • For a good quality video, the overhead is around 20%

17. A Fair Comparison ~ • In order to have a meaningful comparison, need to ensure that each scheme is optimal. • Stream Assignment Algorithm: Determine the reception rate of each receiver by aggregating the data rates of the assigned streams • Rate Allocation Algorithm: Determine the data rate of each stream. • Goal: Maximize the bandwidth utilization by each scheme for a given network, a particular set of receivers and given available bandwidth on the network links

18. System Model • Model the network by a graph G = (V, E) V is a set of routers and hosts E is a set of edges representing connection links. • n is number of receivers • Isolated rate: The reception rate of the receiver if there is no constraint from other receivers in the same session

19. Stream Assignment • Cumulative Layering: Given stream rates αi - Assign as many layers as possible: • Compute the isolated rates • Assign Σi αithat does not exceed the isolated rate.

20. Stream Assignment (Contd.) • Stream replication • Define δ = {δi | δiε R+, i =1,…,m} • δi is the data rate of a replicated stream and m is the number of replicated streams • Set of receivers assigned to stream i. • Two objectives • Minimum reception rate for all receivers is greater than zero • Maximum as much as possible. • Greedy algorithm • Allocate δ1 to all receivers to satisfy the minimum reception rate constraint • Receiver is assigned a stream that has not been assigned and has the maximum value of group size and stream rate product • Receiver can either subscribe to base or any other high quality layer.

21. Stream Assignment (Contd.) • Non-cumulative layering • Define • i is the data rate of a non-cumulatively layered stream and m is the number of streams • Set of receivers assigned to stream i • Two objectives • Minimum reception rate for all receivers is greater than zero • Maximum as much as possible.

22. Stream Assignment Algorithm for Replicated Stream Multicasting • A receiver can subscribe to either the base layer stream or high quality stream

23. Stream Assignment Algorithm for Non-cumulatively layered multicasting • A Receiver can subscribe to multiple streams. The data rate of the aggregated streams leads to the minimum distortion.

24. Rate Allocation • Cumulative layering • Optimal receiver partitioning algorithm determines the optimal rates of layer i, i • Receivers are partitioned into K groups (G1, G2,…, GK) • Objective is to maximize the sum of receiver utilities • Dynamic programming algorithm is used to find an optimal partition • For a given partition, an optimal group transmission rate can be determined • Stream replication • Stream rates, i, are allocated based on the optimal cumulative layering rate. • 1 is the stream rate of the base. If a receiver can join up to k layers, the receiver has the capability to join a replicated stream of data rate k.

25. Rate Allocation (Contd.) • Non-cumulative layering • Receiver can subscribe to any subset of layers without joining the base layer •  = data rate of non-cumulatively layered stream. • Given non-cumulative layered stream ={1,2,4} => selective subscription: isolated rates of {1,2,3,4,5,6,7} • 2m-1 different link capacities with m non-cumulative layers • i are allocated based on i =>

26. Experiments ( Performance Metrics)~ • Average reception rate • Average rate received by a receiver • Average effective reception rate • Amount of data received less the layering overhead • Total bandwidth usage • Adding the total traffic carried by all links in the network for the multicast session • Efficiency • total effective reception rate / total bandwidth usage

27. Network Model • Georgia Tech Internetwork Topology Models (GT-ITM) • 1 server • 1640 nodes with 10 transit domains • 4 nodes per transit domains, 4 stubs per transit node, 10 nodes in a stub domain • transit-to-transit edges = 2.4Gbps • stub-to-stub edges = 10Mbps and 1.5Mbps • transit-to-stub edges = 155Mbps, 45Mbps and 1.5Mbps • number of layers = 8 • amount of penalty = 20%

28. Experiment Results • Random Receiver Distribution - Reception Rate: • Cumulative layering can receive more data • Number of layers in cumulative layering is twice as many as that of non-cumulative layering

29. Experiment Results • Random Receiver Distribution - Effective Reception Rate: • Stream replication has the highest effective reception rate.

30. Experiment Results • Random Receiver Distribution - Total Bandwidth usage: • Cumulative layering has the highest Total Bandwidth Usage.

31. Experiment Results • Random Receiver Distribution - Bandwidth usage efficiency: • Stream Replication has the highest Bandwidth usage Efficiency.

32. Experiment Results • Clustered receiver distribution.

33. Protocol Complexity • Receiver-driven Layered Multicast (RLM) • Receivers decide whether to drop additional layer or not • Join experiment incur a bandwidth overhead • Receivers send a join message and multicast a message identifying the experimental layer to the group • Layered video multicasting • Receiver can join multiple groups • Large multicast group size • Replicated stream video multicasting • Receiver only join one group • Small multicast group size

34. Experimental Results • Average number of groups and average groups size.

35. Experiment Results • The receivers are randomly distributed. • The group size in cumulatively layered video multicasting is twice as large as that in stream replication. • Layered multicasting requires more bandwidth.

36. Experiment Results (Contd.) • Receiver in a cumulatively layered video multicast session requires more buffer size and better synchronization capability than replicated stream video multicasting • Receiver in cumulative layering subscribes to more than five layers on average whereas a receiver in stream replication subscribes to only one stream

37. Conclusion • The Paper has identified the factors affecting relative merits of layering versus replication • Layering penalty • Specifics of the encoding • Protocol complexity • Topological placement • It has developed stream assignment and rate allocation algorithms • And Investigated the conditions under which each scheme is superior • Paper has given a new comparison approach towards video multicast streams

38. Our Comments! • The paper brings up an unbiased support for stream replication approach. • The people supporting only Layered stream multicast approach should re- think. • The paper concludes the support for stream replicating approach based on specific scenarios. • More generalization in experimental scenarios is essential to strengthen the specified support.

39. Questions ??