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  D(t) – Cumulative data consumed by client  B(t) – Maximum cumulative data that can be retrieved by client  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)

8. Algorithm  Construct a feasible piecewise-CBR transmission schedule  Two design principles 1. CBR segments as long as possible 2. When transmission rate must be increased/decreased, change the rate as early as possible

9. Algorithm (a)Client’s buffer will starve (b)Latest time when the client’s buffer is full along the CBR segment (c)Client’s buffer will overflow (d)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 S 1 is smoother than S 2 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