Multiple time scales and subexponentiality in MPEG video streams

We develop a practical, multiple time scale model for MPEG video traffic whose accuracy and relatively low computational complexity make it well suited for real-time traffic generation experiments on broadband networks. The major feature of our approach is the decomposition of the frame size sequence into simple slow and fast time scale components. This accurately captures aspects of queueing behavior that are difficult to model otherwise. The model also exploits the existence of deterministic patterns that are due to the MPEG coding scheme. We also present a novel modeling approach based on spatial renewal processes (SRP). This model gives exact matches to any desired marginal distribution and any convex non-increasing autocorrelation function. In particulax, it can match subexponentially decaying autocorrelations (i.e., can capture long range dependence), something no other model of comparable complexity can do. A SRP is suited for on-line model construction, since it involves no search in parameter spaces, and matches aggregated streams as easily as single streams. The SRP approach yields an analytically tractable queueing behavior, and thus provides a basis for admission control policies that take the dependence structure of video streams into account. The models are validated by queueing simulations.