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Advances in Multimedia
Volume 2012, Article ID 539396, 13 pages
Research Article

High-Definition Video Streams Analysis, Modeling, and Prediction

1Computer Engineering Department, Yarmouk University, Irbid 21163, Jordan
2Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
3Department of Computer Science, Khon Kaen University, Khon Kaen 4002, Thailand

Received 26 November 2011; Revised 7 February 2012; Accepted 7 February 2012

Academic Editor: Marios C. Angelides

Copyright © 2012 Abdel-Karim Al-Tamimi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High-definition video streams' unique statistical characteristics and their high bandwidth requirements are considered to be a challenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and predict high-definition (HD) video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation of over 50 HD video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation for HD videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic resource allocation for video traffic. In addition, we provide a statistical analysis of HD videos, including both factor and cluster analysis to support a better understanding of video stream workload characteristics and their impact on network traffic. We discuss our methodology to collect and encode our collection of HD video traces. Our video collection, results, and tools are available for the research community.