1. A Comparison of Layering and Stream Replication Video Multicast Schemes Taehyun Kim and Mostafa H. Ammar Networking and Telecommunications Group Georgia Institute of Technology Atlanta, Georgia

2. Research Goal  A systematic comparison of video multicasting schemes designed to deal with heterogeneous receivers  Replicated streams  Cumulative layering  Non-cumulative layering

3. Stream Replication  Multiple video streams  Same content with different data rates  Receiver subscribes to only one stream  Example  DSG (Cheung, Ammar, and Li, 1996)  SureStream of RealNetworks   Intelligent streaming of Microsoft 

4. Replicated Stream Multicast

5. Cumulative Layering  1 base layer + enhancement layers  Base layer  Independently decoded  Enhancement layer  Decoded with lower layers  Improve the video quality  Example  RLM (McCanne, Jacobson, Vetterli, 1996)  LVMR (Li, Paul, and Ammar, 1998)  MPEG-2/4, H.263 scalability modes

6. Layered Video Multicast

7. Layering or Replication?  Common wisdom states: “Layering is better than replication”  But it depends on  Layering bandwidth penalty  Specifics of encoding  Protocol complexity  Topological placement of receivers

8. Bandwidth Penalty  Information theoretic results  R(P,  2 )  R(P,  1,  2 )  Packetization overhead  Syntactically independent layering  Picture header  GOP information  Macroblock information

9. Experimental Comparison

10. Comparison by DP J. Kimura, F. A. Tobagi, J. M. Pulido, P. J. Emstad, "Perceived quality and bandwidth characterization of layered MPEG-2 video encoding", Proc. of the SPIE, Boston, MA, Sept. 1999"

11. Providing a Fair Comparison  Need to insure that each scheme is optimized  Two dimensions  Selection of stream/layer rates  Assignments of streams/layers to receivers

12. Rate allocation  Cumulative layering  Optimal receiver partitioning algorithm (Yang, Kim, and Lam)  Stream replication  Cumulative rate allocation

13. Stream assignment  Cumulative layering  Assign as many layers as possible  Stream replication  Greedy algorithm

14. Comparison Methodology  Model of network  Topology  Available bandwidth  Placement of source and receivers  Determine optimal stream rates and allocation  Evaluate performance

15. Performance Metrics  Average reception rate  Total bandwidth usage  Average effective reception rate  Efficiency 

16. Network Topology  GT-ITM  Number of server = 1  Number of receivers = 1,640  Number of transit domains = 10  Number of layers = 8  Amount of penalty = 25%

17. Data reception rate

18. Bandwidth usage

19. Effective reception rate

20. Efficiency

21. Effect of overhead

22. Effect of the number of layers

23. Clustered Distribution  Topology consideration  Layering favors clustered receivers  Stream replication favors randomly distributed receivers  Simulate when receivers are clustered within one transit domain

24. Effective reception rate

25. Protocol Complexity  Layered video multicasting  Multiple join for a receiver  Large multicast group size  Replicated stream video multicasting  One group for a receiver  Small multicast group size

26. Average group size

27. Conclusion  Identified the factors affecting relative merits of layering versus replication  Layering penalty  Specifics of the encoding  Topological placement  Protocol complexity  Developed stream assignment and rate allocation algorithm  Investigated the conditions under which each scheme is superior

28. Optimal Quality Adaptation for MPEG-4 Fine-Grained Scalable Video Taehyun Kim and Mostafa H. Ammar Networking and Telecommunications Group Georgia Institute of Technology Atlanta, Georgia

29. Related Work (1/2)  S. Nelakuditi, et al, “Providing smoother quality layered video stream,” NOSSDAV 2000  Goals  Achieving smoother quality for layered CBR video using receiver buffer  Minimizing quality variation (maximizing runs of continuous frames)

30. Algorithm  Forward scan  Switching between select and discard phase  Entering select phase if buffer is full  Entering discard phase if buffer is empty  Backward scan  Exploiting the residual buffer  Extending each run

31. Bandwidth Model

32. Experimental Result

34. Related Work (2/2)  D. Saparilla, et al, “Optimal streaming of layered video,” INFOCOM 2000  Goal  Investigating the bandwidth allocation problem to minimize loss probability  Modeling the source video and the available bandwidth by stochastic process

35. Main Result  Static policy  Allocating bandwidth in proportion to long run average data rate  Optimal for infinite length, independent layering  Threshold-based policy  If the base layer buffer is below a threshold, allocate bandwidth to the base layer

36. Research Goal of MPEG4 FGS Quality Adaptation  Maximization of the perceptual video quality by minimizing quality variation  Accommodation of the mismatch between  Rate variability of VBR video  Available bandwidth variability

37. MPEG4 FGS Hybrid Scalability  Base layer  Enhancement layer  FGS layer: improving video quality  FGST layer: improving temporal resolution

38. Rate Variability

39. Quality Adaptation Framework C[k]: transmission resource constraint X[k]: cumulative data size S[k]: cumulative selected data size d: threshold

40. Optimal Quality Adaptation  Threshold should be equal to the receiver buffer size to achieve  Minimum quality variability  Necessary condition of maximum bandwidth utilization

41. Online Adaptation  Estimating the threshold point without assuming the available bandwidth information in advance  The available bandwidth is estimated by an MA style linear estimator

42. Experiment Model

43. Bandwidth Variability  TCP  TFRC

44. Performance over TFRC  Threshold-based streaming (Infocom’00)  Online adaptation

45. Performance over TCP  Threshold-based streaming  Online adaptation

46. Conclusion  Accommodated the mismatch between the rate variability and the bandwidth variability  Developed an optimal quality adaptation scheme for MPEG4 FGS video to reduce quality variation  Investigated the perceptual quality of different algorithms and options