Video Traffic Prediction Using Neural Networks

In this paper, we consider video stream prediction for application in services like video-on-demand, videoconferencing, video broadcasting, etc. The aim is to predict the video stream for an efficient bandwidth allocation of the video signal. Efficient prediction of traffic generated by multimedia sources is an important part of traffic and congestion control procedures at the network edges. As a tool for the prediction, we use neural networks - multilayer perceptron (MLP), radial basis function networks (RBF networks) and backpropagation through time (BPTT) neural networks. At first, we briefly introduce theoretical background of neural networks, the prediction methods and the difference between them. We propose also video time-series processing using moving averages. Simulation results for each type of neural network together with final comparisons are presented. For comparison purposes, also conventional (non-neural) prediction is included. The purpose of our work is to construct suitable neural networks for variable bit rate video prediction and evaluate them. We use video traces from [1].