Electrical & Computer Engineering: Faculty Publications

Nonlinear auto-regressive neural network model for forecasting Hi-Def H.265 video traffic over Ethernet Passive Optical Networks

Document Type

Conference Proceeding

Publication Date

5-10-2017

Publication Title

IEEE SoutheastCon 2017 Proceedings

DOI

10.1109/SECON.2017.7925331

Abstract

Video bandwidth forecasting can help optimize the transmission of video traffic over optical access networks. In this paper, we propose the use of a nonlinear auto-regressive (NAR) neural network model for forecasting H.265 video bandwidth requirements to optimize video transmission within Ethernet Passive Optical Networks (EPONs). The video's constituent I, P, and B frames are forecast separately to improve model forecasting accuracy. The proposed forecasting model is able to forecast H.265 encoded High-Definition videos with an accuracy exceeding 90%. In addition, using the video bandwidth requirement predictions as grant requests within EPONs improved the efficiency of dynamic bandwidth allocation (DBA). The use of nonlinear auto-regressive neural network grant sizing predictions within EPONs reduced the video packet queueing delay significantly when the network was saturated near capacity.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored "Nonlinear auto-regressive neural network model for forecasting Hi-Def H.265 video traffic over Ethernet Passive Optical Networks."

Copyright

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