A Monotonic-Decreasing Rate Scheduler for Variable-Bit-Rate Video Streaming

1. A Monotonic-Decreasing Rate Scheduler for Variable-Bit-Rate Video Streaming Hin-lun Lai Yiu-bun Lee Lian-kuan Chen IEEE Transactions on Circuits and System for Video Technology, February 2005

2. Outline • Introduction • MDR (Monotonic-Decreasing Rate) Scheduler • Admission Complexity • Peak Transmission Rate • Client Buffer Requirement • Aggregated MDR Scheduler • Conclusion

3. VBR Video

4. VBR Video Smoothing Downward Adjustment Upward Adjustment B(t) = A(t) + b B(t) ≧S(t) ≧ A(t)

5. Optimal Smoothing Method • MVBA : minimal bit-rate variance • MCBA : minimal change rate

6. Problem • Bandwidth reservation fail : additional bandwidth is not available at upward adjustment • Bandwidth reservation processing delay • Network topology • Reservation protocol • Loss of control message

7. MDR Scheduler • Principle : • eliminate upward adjustment and only use downward adjustment. ri > rj , for j > I • Provide guaranteed video delivery • Advantage : solve previous two problems • Have sufficient system bandwidth • Adjustment processing time will not be critical • Disadvantage : need more client buffer

8. MDR Scheduler {ri , Ti | i = 1, 2, …n}

9. Admission Complexity • Notation • U : System capacity • ui : System utilization at time i • w : video length (seconds) • vj : video transmission bit-rate ( j = 0, 1, …,w-1 ) • A : video startup time • General Optimal Smoothing • ui = ui + vi-A , i = A, A+1, .. A+w-1 (addition computation) • ui + vi-A ≦ U , i = A, A+1, .. A+w-1( comparison computation ) • Need wadditions and w comparisons for a successful admission

10. Admission Complexity • MDR Scheduler • uA + v0 ≦ U (only one comparison) • ui = ui + vi-A , i = A, A+1, .. A+w-1 (addition computation) ui + vi-A , i = A, A+1, …, A+w-1≦ uA + vi-A, ∵ ui is nonincreasing≦ uA+ v0 , ∵ vj ( j = 0, 1, …, w-1) is nonincreasing≦ U For a unsuccessful admission, client will have to wait until the next round to repeat the admission test

11. Peak Transmission Rate • MDR Scheduler has the minimum peak rate among all feasible schedules with zero startup delay. S(t) • Y(t) ≧ A(t) • Y’(t) ≦ S’(t) , for 0 ≦t ≦T1 • Y’(t)＜S’(t) , for 0 ≦t ≦T1 • Y(T1) < S(T1) = A(T1) A(t) Y(t) T1 (Contradiction)

12. Client Buffer Requirement • MDR scheduler generates schedules with the minimum buffer requirement among all feasible monotonic decreasing rate schedules S(t) • X(t) ≧ A(t) • Exist t0 such that S(t0) > X(t0) ≧A(t0) • But S(Ti) = A(Ti), for i=1, 2, …,n • so t0≠ Ti, for i=1, 2, …, n • t0 not in (Ti-1, Ti), for i=2, 3,..n • t0 not exist A(t) t0 T1 X(t) (Contradiction) Buffer

13. MDR Performance Evaluation • Environment • 274 different DVD video • Full length( average 5781 s , and 4348 MB) • 1-Gb/s backbone network Admission Complexity

14. MDR Performance Evaluation

15. Client Buffer Requirement Worst case buffer requirement : 394.5MB

16. Problem in MDR • Client buffer utilization will be low most of time. • The worst-case buffer requirement is unbounded.

17. Aggregated MDR Scheduler • Principle : apply the MDR principle to aggregated network flows. • Method : • Give a fixed client buffer B • If B ≧ video buffer requirement use MDR schedulerelse use optimal smoothing algorithm with B buffer-constrainted • Because server serves many videos simultaneously, so aggregate traffic conforms to the monotonicity property

18. Aggregated MDR Scheduler

19. Admission Complexity • Computation complexity • MDR case : need one comparison • Optimal smoothing case transmission schedule {vi} rate-increasing round : vi > vi-1 increasing round point : hi , i=1, 2, .., g shi+A + vhi ≦ U, for i=0, 1, …g si+A + vi ≦ U, => si+A+1 + vi+1 ≦ U need g+1 comparisons

20. Performance Evaluation Fix buffer size : 32MB

21. Performance Evaluation

22. Performance Evaluation

23. Conclusion • MDR is able to guarantee video delivery with tradeoff in client buffer requirement • The result of AMDR scheduler show that performance is nearly identical to optimal smoothing even for a buffer size as small as 32 MB