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Multimedia Streaming via TCP: An Analytic Performance Study

This paper examines the performance of TCP streaming for live and stored multimedia, exploring the impact of parameters on quality. Through analytic models, simulations, and experiments, it identifies conditions for satisfactory performance and implications for broadband users.

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Multimedia Streaming via TCP: An Analytic Performance Study

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  1. Multimedia Streaming via TCP: An Analytic Performance Study Bing Wang, Jim Kurose, Prashant Shenoy, Don Towsley

  2. Introduction • Under what circumstances can TCP streaming provide satisfactory performance? • Live video streaming is constrained streaming • Stored video streaming is unconstrained streaming

  3. Outline • Analytic model • Simulation • Experiments • Effect of parameters on performance

  4. Contribution of Paper • develop discrete-time Markov models for live and stored video streaming • explore how parameters (i.e. loss rate, round trip time, timeout value & playback rate) affect TCP streaming performance

  5. Assumption • Average TCP throughput is no less than the video bitrate • Startup delay on the order of seconds • Videos are of constant bit rate (CBR)

  6. Performance metrics • Fraction of late packets • no known metric directly for viewing quality

  7. Notations

  8. Model for TCP • time unit is round (length of a round = a round trip time) • Xi is the state of model in the ith round • Xi = (Wi, Ci, Li, Ei, Ri) • Wi = window size • Ci = delayed ACK behavior • Li = # packets lost in (i-1)th round • Ei = backoff exponent if in timeout state • Ri = 0 for new packet; =1 for retransmission

  9. Model for streaming

  10. Model for constrained streaming • Yi is the state of the model in the ith round • Yi = (Xi, Ni) • Xi = state of TCP (mentioned before) • Ni = # early packets

  11. Model for unconstrained streaming • As length go to infinity, # early packets go to infinity and fraction of late packets go to zero • use transient analysis • Yi is the state of the model in the ith round • Yi = Xi

  12. Model for unconstrained streaming • use impulse reward to obtain transient distribution of Ni • impulse reward = difference % # packets received and played back in transition • Ni’= accumulation of the impulse reward up to ith round

  13. Model for unconstrained streaming

  14. Simulation

  15. Simulation

  16. Simulation

  17. Simulation

  18. Simulation

  19. Simulation

  20. Simulation

  21. Simulation

  22. Experiments

  23. Experiment

  24. Experiment

  25. Exploring parameter space

  26. Effect of video length

  27. Effect of T/μ

  28. Sensitivity to parameters

  29. Conditions for satisfactory performance

  30. Summary of results • fraction of late packets increases with length in live streaming, but decreases with length in stored streaming • performance increases with T/μ; beyond a certain point yields diminishing gain • performance is not solely determined by T/μ but also sensitive to parameters like R, p, T0 • For large R, p and T0, either long startup delay or T/μ greater than 2 is needed for low fraction of late packets

  31. Implication • large fraction of streaming video clips are encoded at 300Kbps • most DSL and cable modem connection support 750Kbps – 1.5Mbps • TCP streaming is adequate for broadband users

  32. Conclusion • Discrete-time Markov models for live and stored video streaming • Simulation and experiments show models are accurate • Study effect of various parameters on performance with the models

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