Nonlinear traffic modeling of VBR MPEG-2 video sources

Abstract

A neural network scheme is presented for modeling VBR MPEG-2 video sources. In particular, three nonlinear autoregressive models (NAR) are proposed to model the aggregate MPEG-2 video sequence, each of which corresponds to one of the three types of frames (I, P and B frames). Then, the optimal mean-squared error predictor of the NAR model is implemented using a feedforward neural network with a tapped delay line (TDL) filter. A novel algorithm is also introduced, which handles the significant effect of the correlation among the I, P and B frames on the estimation of network resources. Furthermore, a new mechanism is proposed to improve the modeling accuracy, especially at high bit rates, based on a generalized regression neural network. Experimental studies and computer simulations illustrate the efficiency and robustness of the proposed model as predictor of the network resources compared to conventional models.