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By James D. salehi, Zhi-Li Zhang, James F. Kurose, and Don Towsley, PowerPoint Presentation
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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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slide1

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
  • Introduction
  • Optimal Smoothing
  • Smoothness
  • Impact on network resources requirements
  • Conclusion
introduction
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
reducing rate variability
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
reducing rate variability1
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
optimal smoothing
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
  • 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
  • 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
  • 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
  • Optimal Smoothing of a 2-hour MPEG-1 encoding movie with 500 ms startup latency
smoothness
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
smoothness1
Smoothness
  • 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
  • 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
  • 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
rcbr model
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
conclusion
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