Traffic prediction and network resources estimation of VBR MPEG-2 sources using adaptively trained neural networks

In this paper, a unified non-linear modeling is proposed appropriate both for on-line traffic prediction and network resources estimation in the case of VBR MPEG-2 coded video sources. A feedforward neural network architecture with tapped delay inputs is adopted to implement the nonlinear model structure. For on-line traffic modeling, a weight adaptation algorithm is activated, to modify the model parameters, in the case of abrupt changes of the traffic statistics, where the local characteristics may change, Furthermore, in the case of off-line traffic modeling, where the system is used as network resource estimator, an error correlation mechanism is proposed to relate the rates of the I, P and B frames. Experimental results with real life video sequences of long duration are presented to show the good performance of the proposed scheme both as traffic rate predictor and network resource estimator.