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Short‐term MPEG‐4 video traffic prediction using ANFIS
Author(s) -
Abdennour Adel
Publication year - 2005
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.578
Subject(s) - computer science , adaptive neuro fuzzy inference system , real time computing , term (time) , bandwidth (computing) , data mining , artificial intelligence , fuzzy logic , computer network , fuzzy control system , physics , quantum mechanics
Abstract Multimedia traffic and particularly MPEG‐coded video streams are growing to be a major traffic component in high‐speed networks. Accurate prediction of such traffic enhances the reliable operation and the quality of service of these networks through a more effective bandwidth allocation and better control strategies. However, MPEG video traffic is characterized by a periodic correlation structure, a highly complex bit rate distribution and very noisy streams. Therefore, it is considered an intractable problem. This paper presents a neuro‐fuzzy short‐term predictor for MPEG‐4‐coded videos. The predictor is based on the Adaptive Network Fuzzy Inference System (ANFIS) to perform single‐step predictions for the I, P and B frames. Short‐term predictions are also examined using smoothed signals of the video sequences. The ANFIS prediction results are evaluated using long entertainment and broadcast video sequences and compared to those obtained using a linear predictor. ANFIS is capable of providing accurate prediction and has the added advantage of being simple to design and to implement. Copyright © 2005 John Wiley & Sons, Ltd.