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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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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
Agenda End-toEnd Resource Requirements through Optimal Smoothing

  • Introduction

  • Optimal Smoothing

  • Smoothness

  • Impact on network resources requirements

  • Conclusion


Introduction
Introduction End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Reducing rate variability
Reducing rate variability End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Reducing rate variability1
Reducing rate variability End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Optimal smoothing
Optimal Smoothing End-toEnd Resource Requirements through 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


Algorithm
Algorithm End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Algorithm1
Algorithm End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Algorithm2
Algorithm End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Evaluation
Evaluation End-toEnd Resource Requirements through Optimal Smoothing

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


Smoothness
Smoothness End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Smoothness1
Smoothness End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Impact on network resource
Impact on network resource End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Guaranteed service model
Guaranteed Service Model End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Guaranteed service model1
Guaranteed Service Model End-toEnd Resource Requirements through Optimal Smoothing


Rcbr model
RCBR Model End-toEnd Resource Requirements through Optimal Smoothing

  • 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


Rcbr model1
RCBR Model End-toEnd Resource Requirements through Optimal Smoothing


Conclusion
Conclusion End-toEnd Resource Requirements through Optimal Smoothing

  • 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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