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    block this user Raj Jain Trusted member

    Professor / jain@acm.org

    Washington University in Saint Louis

    High-Definition Video Streams Analysis, Modeling, and Prediction

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    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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    Description

    Title : High-Definition Video Streams Analysis, Modeling, and Prediction
    Author(s) : Abdel-Karim Al-Tamimi, Raj Jain, and Chakchai So-In
    Abstract : 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.
    Keywords : Future Wireless Networks, FWNs, Future Internet, Future Wireless Internet, Next Generation Wireless Networks, NGWNs, Mobility, Multihoming, Location Privacy, Multi-Interface Selection, ID/Locator Split, Network Architectures

    Subject : Video Modeling
    Area : Computer Science
    Language : English
    Year : 2012

    Affiliations Washington University in Saint Louis
    Journal : Advances in Multimedia
    Volume : 2012
    Publisher : Hindawi Publishing Corporation
    City : New York
    Pages : 13
    Url : http://downloads.hindawi.com/journals/am/2012/539396.pdf
    Doi : 10.1155/2012/539396

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    Raj's Peer Evaluation activity

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