Application of chaos theory to the modeling of compressed video

We apply nonlinear chaos theory in modeling and forecasting variable-bit-rate (VBR) video sequences. Nonlinear chaos modeling offers an alternative approach to stochastic (typically, linear) approaches, with the advantages of lower dimensionality and more determinism. However, the goodness of its predictions strongly depends on the accuracy with which the dimensionality of a chaotic model is estimated from empirical data. The contributions of this paper are twofold. First, we present a novel approach for estimating the embedding dimension of any chaotic time series that satisfies the functional relationship of Farmer and Sidorowich (1987). The proposed approach is applied to VBR video data and is used to show the existence of chaos in packetized video traffic. Second, we develop a chaos-theory-based model for VBR intracoded video, which can be used to generate a rich set of synthetic traces that exhibit similar statistical structure to the original data. These traces are useful in performance evaluation and resource allocation in integrated computer networks