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A Comparison of Layering and Stream Replication Video Multicast Schemes

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. Research Goal.

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A Comparison of Layering and Stream Replication Video Multicast Schemes

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  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, D2)  R(P, D1, D2) • 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

  33. 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. TCP TFRC Bandwidth Variability

  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

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