Statistical modeling of MPEG coded video

The major driving force behind the emerging of broadband integrated networks is traffic from video sources. Since this traffic will be a substantial portion of the overall traffic carried by the integrated networks, it is extremely important that adequate source models are found. The networks´ dimensioning depends on the statistical properties of traffic sources. For video sources the Moving Picture Experts Group (MPEG) compression scheme was proposed several years ago and it has become the de facto standard for video compression since then. However, even with the huge reduction of bits that MPEG compression provides, it does not smooth the video traffic. Indeed the MPEG compression algorithm guarantees that the MPEG stream will be bursty. The aim of the work outlined was finding a suitable model for the MPEG coded video traffic. The paper concentrates on a variety of models and their capabilities to model adequately the statistical properties of MPEG video. In particular, the transform-expand-sample (TES), the autoregressive (AR), the autoregressive moving average (ARIMA) and the first order statistics histogram models were investigated in terms of both accuracy and complexity. A short discussion regarding the tradeoff between the accuracy and the complexity of these models is also provided.