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Investigators
Z. Obradovic, R. Drossu, and C. Raghavendra, T.V. Lakshman
Problem
Accurate traffic prediction can be used to optimally smooth delay
sensitive traffic and increase multiplexing gain in asynchronous
transfer mode (ATM) networks.
The objectives of this work are to investigate applicability of
neural network techniques for prediction of either the following
or several following frame sizes using the information of previous
frame sizes.
Results
Neural network models are considered for two important types of
video sequences - video teleconferencing and entertainment video.
An off-line learning method is suggested for simple traffic (drossu94)
and an on-line learning method for complex one (drossu96book).
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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