Single and Multiple Frame Video Traffic Prediction Using Neural Network Models

The objectives of this paper are to investigate applicability of neural network techniques for single and multiple frame video traffic prediction. In the single and multiple frame traffic prediction problems, the information of previous frame sizes is used to predict either the following or several following frame sizes respectively. Accurate traffic prediction can be used to optimally smooth delay sensitive traffic [Ott et al., 1992] and increase multiplexing gain in asynchronous transfer mode (ATM) networks. Neural network models for both single and multiple frame traffic prediction problems are proposed. Two important types of video sequences are considered - video teleconferencing and entertainment video. An off-line learning method is suggested for simple traffic and an on-line learning method for complex one. Simulation studies of cell losses in an ATM multiplexer using recorded variable-bit-rate coded video teleconference data indicate reasonably good predictions for buffer delays between 0.5 and 5 ms.

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