@inproceedings{038ece39ea5e44afbe1fce0f52764702,
title = "Nonlinear auto-regressive neural network model for forecasting Hi-Def H.265 video traffic over Ethernet Passive Optical Networks",
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.",
keywords = "EPON, H.265, NAR, Neural network., Nonlinear Auto-regressive, Video forecasting, Video prediction",
author = "Collin Daly and Moore, {David L.} and Haddad, {Rami J.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; IEEE SoutheastCon 2017 ; Conference date: 30-03-2017 Through 02-04-2017",
year = "2017",
month = may,
day = "10",
doi = "10.1109/SECON.2017.7925331",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SoutheastCon 2017",
address = "United States",
}