A MARKOVIAN MODEL OF CODED VIDEO TRAFFIC WHICH EXHIBITS LONG-RANGE DEPENDENCE IN STATISTICA LANALYSIS

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概要

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• The purpose of this paper is to construct a Markovian model generating a sequence having almost the same statistical characteristics as a real video traffic process. We deal with measured traffic data from a certain video source. Taking scene changes into account we analyze the data and construct a model, called Markov-AR model, composed of three submodels; a Markov transition model for scene changes, an AR model for spikes of scenes, and an AR model with random parameters for bit rate sequences of individual scenes. A simulation study shows that statistical characteristics of a sequence generated by this model are very similar to the actual video traffic. Especially, in the variance-time analysis and in the R/S analysis, these two sequences give similar estimates for the Hurst parameters and exhibit long-range dependence even though the model consists of only Markovian type, short-range dependent processes.

• 社団法人日本オペレーションズ・リサーチ学会の論文

著者

• Takahashi Yukio

  Tokyo Institute of Technology

• Kobayashi K

  Nec Corporation

• Kurasugi Toshiyasu

  NEC Corporation

• Kobayashi Kazutomo

  NEC Corporation

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• A Backlog Evaluation Formula for Admission Control Based on the Stochastic Network Calculus with Many Flows
• A MARKOVIAN MODEL OF CODED VIDEO TRAFFIC WHICH EXHIBITS LONG-RANGE DEPENDENCE IN STATISTICA LANALYSIS

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