Optimal H-infinity prediction algorithm for uncertain non-stationary real-time MPEG VBR traffic

Abstract

In this paper, a prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodicity characteristics of MPEG video traffic are fully captured by a proposed state-space stochastic dynamic model, which includes traffic parameters in state vector, to improve the accuracy of prediction. As the statistics of the underlying processes are either unavailable or uncertain in real-time applications, a recursive H,,. filtering algorithm is proposed to estimate traffic parameters for long-range prediction. Unlike previous prediction schemes, which predict I, P and B frames separately, the proposed scheme predicts the composite MPEG video traffic. Simulation results based on real MPEG traffic data show that the time-varying trend, the periodic components, and the long-range dependence property can be splendidly predicted and captured by the proposed method. The proposed scheme has a superior performance than the conventional methods, such as LMS, RLS, and TDNN algorithms, in long-range prediction.