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By James D. salehi, Zhi-Li Zhang, James F. Kurose, and Don Towsley,

Supporting Stored Video: Reducing Rate Variability and End-toEnd Resource Requirements through Optimal Smoothing. By James D. salehi, Zhi-Li Zhang, James F. Kurose, and Don Towsley, Univerity of Massachusetts, USA. Agenda. Introduction Optimal Smoothing Smoothness

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By James D. salehi, Zhi-Li Zhang, James F. Kurose, and Don Towsley,

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  1. Supporting Stored Video: Reducing Rate Variability and End-toEnd Resource Requirements through Optimal Smoothing By James D. salehi, Zhi-Li Zhang, James F. Kurose, and Don Towsley, Univerity of Massachusetts, USA

  2. Agenda • Introduction • Optimal Smoothing • Smoothness • Impact on network resources requirements • Conclusion

  3. Introduction • VBR encoded video • Lower average bit rate compared to CBR • Exhibits significant rate variability • Makes resources management difficult • Three techniques for reducing rate variability • Temporal Multiplexing • Statistical Multiplexing • Smoothing by work-ahead

  4. Reducing rate variability • Temporal Multiplexing • Introduce a per-stream buffer along the end-to-end path • When the rate is too high • Video data is buffered along the path • Delay is introduced • Statistical Multiplexing • Multiple independent streams share single resource • Gain due to statistical behavior of different stream • Supports streams with summed peak rate > bandwidth

  5. Reducing rate variability • Smoothing by work-ahead • Video data ahead of schedule is sent if • The data is available to be sent • The client has sufficient buffer space to retrieve

  6. Optimal Smoothing • Smoothing by work-ahead technique • Optimal in the sense of • The greatest possible reduction in rate variability • The video data is sent “as smooth as” possible • Lowest peak rate and lowest variance • Smooth defined by using majorization* *A. W. Marshall and I. Olkin. “Inequalities: Theory of Majorization and its Applications”. New York, Academic Press, 1979

  7. Algorithm • Transmission schedule • A vector of [a(1),…a(N)] where a(t) is the amount of data sent at time t • A feasible schedule is any schedule that lies between D(t) and B(t) • D(t) – Cumulative data consumed by client • B(t) – Maximum cumulative data that can be retrieved by client

  8. Algorithm • Construct a feasible piecewise-CBR transmission schedule • Two design principles • CBR segments as long as possible • When transmission rate must be increased/decreased, change the rate as early as possible

  9. Algorithm • Client’s buffer will starve • Latest time when the client’s buffer is full along the CBR segment • Client’s buffer will overflow • Latest time at which the client’s buffer is empty along the CBR segment

  10. Evaluation • Optimal Smoothing of a 2-hour MPEG-1 encoding movie with 500 ms startup latency

  11. Smoothness • What is smooth? • Majorization • X and Y are two vectors of length n with elements sorted descendingly • X is majorized by Y or • Example: X =[3,3,2,2] and Y=[8,1,1,0], • Measures which vector has more “evenly distributed” elements • Less general measures of variability

  12. Smoothness • Transmission schedule S1is smoother than S2 if • Optimal Smoothing generates a schedule S* • For any feasible schedule S, S*S • Optimal Smoothing is smoothest in the sense of majorization

  13. Impact on network resource • Evaluate the benefit of Optimal Smoothing in two models • Deterministic Guaranteed service • Benefits under bounded delay service • End-to-End delay through the network is guaranteed • Renegotiated CBR service • Server can renegotiate bandwidth when rate changes

  14. Guaranteed Service Model • Bounded-delay Guaranteed Service Model • All streams forwarded to the same link • A new stream is admitted into the network if it can guarantee that the delay bound will never be exceeded • Q = maximum no. of bits that can arrive from all the streams – no. of bits that can be served • A(1) = time to clear the largest possible packet • C = Link capacity

  15. Guaranteed Service Model

  16. RCBR Model • Maximum no. of renegotiation allowed = R • Evaluation done by • Identify a minimum cost reservation schedule for the smoothed video with R or fewer renegotiations • Every stream will renegotiate bandwidth with the generated reservation schedule • Find the maximum no. of streams that can be supported such that aggregate maximum bandwidth does not exceed link capacity

  17. RCBR Model

  18. Conclusion • Optimal smoothing generates smooth transmission schedule • Under specific network studied, no. of streams supported can be double • Optimal smoothing can be done offline • Optimal smoothing still generates a VBR traffic

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