Open AccessHigh-Definition Video Streams Analysis, Modeling, and Prediction
Author(s) -
Abdel-Karim Al-Tamimi,
Raj Jain,
Chakchai So–In
Publication year - 2012
Publication title -
advances in multimedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.278
H-Index - 17
eISSN - 1687-5699
pISSN - 1687-5680
DOI - 10.1155/2012/539396
Subject(s) - computer science , workload , encode , real time computing , autoregressive integrated moving average , encoding (memory) , data mining , scheduling (production processes) , artificial intelligence , machine learning , time series , biochemistry , chemistry , operations management , economics , gene , operating system
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
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