A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic

This paper investigates the long-term prediction of MPEG video traffic Predicting such traffic over a long horizon is important for today's fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored espec...

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Main Author: Adel Abdennour
Format: Article
Language:English
Published: Springer 2005-01-01
Series:Journal of King Saud University: Engineering Sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S1018363918308274
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author Adel Abdennour
author_facet Adel Abdennour
author_sort Adel Abdennour
collection DOAJ
description This paper investigates the long-term prediction of MPEG video traffic Predicting such traffic over a long horizon is important for today's fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored especially for MPEG-4 coded videos despite its effectiveness m a number of important network-edge applications such as dynamic bandwidth allocation, quality of service (QoS) control, and network management and planning. The main reason for the shortage of publication, in such area is the difficulty of the problem, especially when classical or widely used prediction techniques are the ones to be employed. Prediction results, in this paper, are obtained using a simple m:uro-fuvy system and are compared to the classical normalized Least Mean Squares (LMS) technique. The Neuro-fuzzy predictor is capable of predicting various real MPEG-4 real-world video traffic hundreds of frames m advance with high accuracy.
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institution Kabale University
issn 1018-3639
language English
publishDate 2005-01-01
publisher Springer
record_format Article
series Journal of King Saud University: Engineering Sciences
spelling doaj-art-034632587a2e42b2a6afb2cd82a1818b2025-08-20T03:55:32ZengSpringerJournal of King Saud University: Engineering Sciences1018-36392005-01-0118116117910.1016/S1018-3639(18)30827-4A Long Horizon Neuro-fuzzy Predictor for MPEG Video TrafficAdel Abdennour0Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800. Riyadh 11421, Saudi ArabiaThis paper investigates the long-term prediction of MPEG video traffic Predicting such traffic over a long horizon is important for today's fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored especially for MPEG-4 coded videos despite its effectiveness m a number of important network-edge applications such as dynamic bandwidth allocation, quality of service (QoS) control, and network management and planning. The main reason for the shortage of publication, in such area is the difficulty of the problem, especially when classical or widely used prediction techniques are the ones to be employed. Prediction results, in this paper, are obtained using a simple m:uro-fuvy system and are compared to the classical normalized Least Mean Squares (LMS) technique. The Neuro-fuzzy predictor is capable of predicting various real MPEG-4 real-world video traffic hundreds of frames m advance with high accuracy.http://www.sciencedirect.com/science/article/pii/S1018363918308274
spellingShingle Adel Abdennour
A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
Journal of King Saud University: Engineering Sciences
title A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
title_full A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
title_fullStr A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
title_full_unstemmed A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
title_short A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic
title_sort long horizon neuro fuzzy predictor for mpeg video traffic
url http://www.sciencedirect.com/science/article/pii/S1018363918308274
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