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Scheduling for Variable-Bit-Rate Video Streaming

By H. L. Lai

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Contents

Variable-Bit-Rate VideosBit-Rate SmoothingMonotonic Decreasing Rate SchedulingAggregated Monotonic Decreasing Rate SchedulingConclusionsQ&A

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Variable-Bit-Rate Videos

CBR vs. VBRProblems with VBR

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CBR vs. VBR

2 types of video compression:CBR compression Constant bit-rate Variable visual quality

VBR compression Variable bit-rate Constant visual quality

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Problems with VBR

Complex admission control and schedulingHard to provide performance guarantee

Solution: Smoothing

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Bit-Rate-Smoothing

PrincipleDesign ConsiderationsReview of smoothing algorithms

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Principle

Time

Acc

umul

ated

Dat

a

TransmissionSchedule, S(t )

Client Buffer Size

Cumulative DataConsumption Function, A(t )

Overflow Limit, B(t )

L

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Design Considerations

Lossless or lossy video?Stored video or live video?Zero or non zero playback delay?Deterministic or statistical performance guarantee?

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Optimal Smoothing Algorithm

J. D. Salehi, S.-L. Zhang, J. Kurose, and D. Towsley, “Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing”, IEEE/ACM Transactions on Networking, pp. 397-410, vol. 6, issue 4, Aug. 1998.

Minimal variabilityMinimal peak rate

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Piecewise Constant Rate Transmission and Transport

J. McManus and K. Ross, “Video on demand over ATM: constant-rate transmission and transport”, Proceedings of IEEE INFOCOM, pp. 1357-1362, Mar. 1996.

Control the separation and no. of bit-rate changes

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CBA & MCBA

Critical Bandwidth Allocation (CBA)W. Feng and S. Sechrest, “Critical bandwidth allocation for the delivery of compressed video”, Computer Communications, pp. 709-717, vol. 18, no. 10, Oct. 1995.

Minimum changes Bandwidth Allocation (MCBA)W. Feng, F. Jahanian and S. Sechrest, “Optimal buffering for the delivery of compressed prerecorded video”, ACM Multimedia Systems Journal, Sep. 1997

Minimal peak rateMinimal BW increases (CBA)Minimal BW changes (MCBA)

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Rate Constrained Bandwidth Allocation

W. Feng, “Rate-constrained bandwidth smoothing for the delivery of stored video”, SPIE Multimedia Networking and Computing, pp. 58-66, Feb. 1997.

Check all frame sizesPrefetch earlier if any frame exists BW constraint

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Time Constrained Bandwidth Allocation

W. Feng, “Time constrained bandwidth smoothing for interactive video-on-demand systems”, International Conference on Computer Communications, pp. 291-302, Nov. 1997.

Construct an upper bound curve with both buffer and time constraintsConstruct schedule with any other smoothing algorithms

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ON-OFF Scheduling

R.-I Chang, M. C. Chen, J.-M. Ho and M.-T. Ko, “Designing the ON-OFF CBR transmission schedule for jitter-free VBR media playback in real-time networks”, Proceedings of the Fourth International Workshop on Real-Time Computing Systems and Applications, pp. 2-9, Oct. 1997.

Single rate for whole systemSend “as late as possible”

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Other StudiesSmoothing at multiple intermediate nodes

J. Zhang, “Using multiple buffers for smooth VBR video transmissions over the network”, 1998 International Conference on Communication Technology, pp. 419-423,

vol. 1, Oct. 1998. Multiplexing optimally smoothed schedules

W. Zhao and S. K. Tripathi, “Bandwidth-efficient continuous media streaming through optimal multiplexing”, Proceedings of International Conference on Measurement and Modeling of Computer Systems, pp. 13-22, Apr. 1999.S. S. Lam, S. Chow and D. K. Y. Yau, “A lossless smoothing algorithm for compressed video”, IEEE/ACM Transactions

on Networking, pp. 697-708, vol. 4, issue 5, Oct. 1996. Scene based smoothing

H. Liu, N. Ansari and Y.-Q. Shi, “Dynamic bandwidth allocation for VBR video traffic based on scene change identification”, Proceedings of International Conference on

Information Technology: Coding and Computing, pp. 284-288, March 2000. Re-arranging sending sequence of frames

R. Sabat and C. Williamson, “Cluster-based smoothing for MPEG-based video-on-demand systems”, IEEE International Conference on Performance, Computing and Communications, pp. 339-346, Apr. 2001.

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Other Studies (cont.)

Lossless online smoothingJ. Rexford, S. Sen, J. Dey, W. Feng, J. Kurose, J. Stankovic and D. Towsley, “Online Smoothing of Live, Variable-Bit-Rate Video”, International Workshop on Network and Operating Systems Support for Digital Audio and Video, pp. 249-258, May, 1997.

Controlling online encoding parameters with: Buffer occupancy

S. C. Liew and D. C.-Y. Tse, “A control-theoretic approach to adapting VBR compressed video for transport over a CBR communications channel”, IEEE/ACM Transactions on Networking, pp. 42-55, vol. 6, issue 1, Feb. 1998.

Network statusN. G. Duffield, K. K. Ramakrishnan, and A. R. Reibman, “SAVE: an algorithm for smoothed adaptive video over explicit rate network”, IEEE/ACM Transactions on Networking, pp. 717-728, vol. 6, issue 6, Dec. 1998.

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Monotonic Decreasing Rate Scheduler

MotivationConstructing an MDR SchedulePerformance EvaluationAdmission ComplexityWaiting Time vs. System UtilizationBuffer requirement

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Motivation

Existing smoothing algorithms contains both upward and download bandwidth changes Complex admission to provide deterministic

performance guarantee Upward changes may fail in mixed traffic

environments

Solution: transmission with downward bandwidth changes only – MDR Scheduler

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Constructing an MDR Schedule

Time

Acc

umul

ated

Dat

a

MDR schedule S(t)

First bit-rate reduction point: T1

A(t)

r1

r2

r3

Second bit-rate reduction point: T2

ri: transmission rate for segment i

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Performance Evaluation

274 VBR encoded DVD videos testedAvg. bit-rate: 6.01MbpsAvg. length: 5780.7sRound length: 1sRequests generated according to Poisson process to select a random videoUn-admitted requests put to FIFO queue

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Admission Complexity

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Waiting Time vs. System Utilization

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90

utilization(%)

wa

itin

g ti

me

(s)

1Gbps OS

1Gbps MDR

500Mbps OS

500Mbps MDR

100Mbps OS

100Mbps MDR

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Buffer Requirement

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300 350 400

Client Buffer Size (MB)

Cum

ulat

ive

Pro

port

ion

of V

ideo

s

(140MB)

(0.92)

(0.78)

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Aggregated Monotonic Decreasing Rate Scheduler

PrincipleBandwidth Over-allocationAdmission ComplexityPerformance EvaluationEffect of Network Topology

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Principle

Specify a buffer requirement, BFor streams with buffer requirement: <= B, deliver with MDR schedules > B, deliver with optimal smoothing and

over-allocate bandwidth to maintain monotonicity of the aggregate system traffic

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Bandwidth Over-allocation

++

==

New exceptional stream,smoothed using optimal smoothing.

Current aggregate bandwidth utilization.

Aggregate bandwidth utilization and reservation after new stream is admitted.

Bandwidth over-allocated here to maintain rate monotonicity .

Time

Time

Time

Rat

eR

ate

Rat

e

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Admission Complexity

Unsuccessful admissioncomparisons = additions:

O(α+(1−α)(g+1)) = O(1+(1−α)g)

Successful admissioncomparisons:

O(α +(1−α)(g+1+w)) = O(1+(1−α)(g+w)) additions:

O(w)

Where: α is the proportion of videos served by MDRS g is the no. of bit-rate increases in optimal smoothing

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Admission Complexity (cont.)With 16M of client buffer

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Admission Complexity (cont.)With 32M of client buffer

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Admission Complexity (cont.)With 64M of client buffer

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Waiting Time vs. Client Buffer Size (cont.)

0

5

10

15

20

25

30

35

40

45

0 50 100 150 200 250buffer (Mbytes)

wa

itin

g ti

me

(s)

r=.9 AMDR

r=.9 OS

r=.9 MDR

r=.8 AMDR

r=.8 OS

r=.8 MDR

r=.7 AMDR

r=.7 OS

r=.7 MDR

r=.6 AMDR

r=.6 OS

r=.6 MDR

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Waiting Time vs. Client Buffer Size

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Waiting Time vs. System Capacity

To be completed…

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Effect of Network Topology

In practice, network topologies are likely to be more complexWe simulate a network with two-level, tree-based topologyThe effect of maintaining monotonicity within each individual branch is studiedResults: to be completed…

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Conclusions

Scheduling of VBR video streaming is a complex problemSmoothing can reduce the variability; but will not completely solve the problemThe MDR Scheduler can provide deterministic guarantee with low admission complexityPerformance is comparable optimal smoothingWith a trade off in performance and complexity, the AMDR Scheduler adapt to any buffer size

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Q&A

Thank you