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Video Modeling

Video Modeling. Pravin Rajamoney CSE-581 Network Technology. Papers:. Analysis, Modeling and Generation of Self-Similar VBR Video Traffic. M.W.Garrentt and W.Willinger

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Video Modeling

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  1. Video Modeling Pravin Rajamoney CSE-581 Network Technology

  2. Papers: • Analysis, Modeling and Generation of Self-Similar VBR Video Traffic. M.W.Garrentt and W.Willinger • The Correlation Structure for a Class of Scene-Based Video Models and Its Impact on the Dimensioning of Video Buffers. M.M.Krunz and A.M.Ramasamy • Hurst Parameter Estimation of Long-Range Dependent VBR MPEG Video Traffic in ATM Networks. S.H.Hong, R.Park and C.B.Lee • Simple and Efficient Models for Variable Bit Rate MPEG Video Traffic. O.Rose

  3. Acronyms MPEG Moving Pictures Expert Group VBR Variable Bit Rate CBR Constant Bit Rate GOP Group of Pictures ATM Asynchronous Transfer Mode SRD Short Range Dependent LRD Long Range Dependent

  4. 2 min review on MPEG-2 Video Theory GOP = 12 IBBPBBPBBPBB I Picture = Intra coded pictures P Picture = Predictive coded pictures B Picture = Bi-directionally coded pictures

  5. MPEG-2 Video Theory Field rate 2 fields per frame Frame rate 29.97 frames per second (US) NTSC 25 frames per second (Europe) PAL Less for computers Spatial encoding Temporal encoding

  6. CBR vs. VBR CBR video Advantage: • Fluid flow video model • easier buffer management • easier on the network Disadvantage: • Not bandwidth efficient e.g. If average video bandwidth is 1.5Mbps, but its spike are as high as 3.5Mbps. Network must always guarantee 3.5Mbps

  7. CBR vs. VBR VBR video Advantage: • Bandwidth efficient • Bursty Disadvantage: • Difficult to model • Buffer management required • Data rate control required

  8. Why model VBR video? • Simulation • Analyze the stream for a particular network.

  9. How do you make sense out of this?

  10. Types of video modeling • Probability density of Gamma/Pareto model ( modified bell shape) • Scene-oriented model • Markov chain model • Histogram model (0th order Markov chain)

  11. Gamma/Pareto model

  12. Short Range Dependence (SRD) • Short time scale 10ms •  200 frames • Markov chain model, ARIMA process

  13. Long Range Dependent (LRD) • Synonymous for “Hurst effect” • Also know as “persistence phenomena” Observation of an empirical record being significantly correlated to observation that are far removed in time

  14. Hurst value: 0.5 - ~0.75 Low activity ~0.75 - ~0.9 Medium activity ~0.9 - 1 High activity Hurst parameter is related to the amount of motion involved in the sequence

  15. Why simulate VBR video? • Calculate minimum reservation rate. R* • Amount of buffering needed in the system for it not to overflow

  16. Why simulate VBR video? BANDWIDTH Bitrate Bandwidth utilization

  17. Conclusion • LRD must be taken into consideration when modeling VBR video • VBR video is content dependent • Bandwidth and buffer size depends on the video mean bit rate • ATM systems: Peak rate Sustain rate Average VBR rate • Network characterization, for real-time VBR video.

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