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

A Monotonic-Decreasing Rate Scheduler for

Variable-Bit-Rate Video Streaming

Hin-lun Lai

IEEE Transactions on Circuits and System for Video Technology, February 2005

Yiu-bun Lee Lian-kuan Chen

Outline

Introduction MDR (Monotonic-Decreasing Rate)

Scheduler– Admission Complexity– Peak Transmission Rate– Client Buffer Requirement

Aggregated MDR Scheduler Conclusion

VBR Video

VBR Video Smoothing

B(t) = A(t) + b

B(t) ≧ S(t) ≧ A(t)

Upward Adjustment

Downward Adjustment

Optimal Smoothing Method

MVBA : minimal bit-rate variance MCBA : minimal change rate

Problem

Bandwidth reservation fail : additional bandwidth is not available at upward adjustment

Bandwidth reservation processing delay– Network topology– Reservation protocol – Loss of control message

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

MDR Scheduler

{ri , Ti | i = 1, 2, …n}

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 w additions and w comparisons for a successful admission

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

Peak Transmission Rate

MDR Scheduler has the minimum peak rate among all feasible schedules with zero startup delay.

(1) Y(t) ≧ A(t)

(2) Y’(t) ≦ S’(t) , for 0 ≦t ≦T1

(3) Y’(t) ＜ S’(t) , for 0 ≦t ≦T1

(4)

(5) Y(T1) < S(T1) = A(T1)

S(t)

A(t)

Y(t)

T1

(Contradiction)

Client Buffer Requirement

MDR scheduler generates schedules with the minimum buffer requirement among all feasible monotonic decreasing rate schedules

(1) X(t) ≧ A(t)

(2) Exist t0 such that S(t0) > X(t0) ≧A(t0)

(3) But S(Ti) = A(Ti), for i=1, 2, …,n

(4) so t0 ≠ Ti, for i=1, 2, …, n

(5) t0 not in (Ti-1, Ti), for i=2, 3,..n

(6) t0 not exist

S(t)

X(t)

A(t)

T1

t0

Buffer(Contradiction)

MDR Performance Evaluation Environment

– 274 different DVD video– Full length( average 5781 s , and 4348 MB)– 1-Gb/s backbone network

Admission Complexity

MDR Performance Evaluation

Client Buffer Requirement

Worst case buffer requirement : 394.5MB

Problem in MDR

Client buffer utilization will be low most of time.

The worst-case buffer requirement is unbounded.

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

Aggregated MDR Scheduler

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

Performance Evaluation

Fix buffer size : 32MB

Performance Evaluation

Performance Evaluation

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