Workload models of VBR video traffic and their use in resource allocation policies
IEEE/ACM Transactions on Networking (TON)
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Statistical properties of MPEG video traffic and their impact on traffic modeling in ATM systems
LCN '95 Proceedings of the 20th Annual IEEE Conference on Local Computer Networks
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic bandwidth allocation based on online traffic prediction for real-time MPEG-4 video streams
EURASIP Journal on Applied Signal Processing
Modeling and generation of AVC and SVC-TS mobile video traces for broadband access networks
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Modeling and resource allocation for mobile video over WiMAX broadband wireless networks
IEEE Journal on Selected Areas in Communications
Modeling and Prediction of High Defninition Video Traffic: A Real-World Case Study
MMEDIA '10 Proceedings of the 2010 Second International Conferences on Advances in Multimedia
Supporting real time VBR video using dynamic reservation based on linear prediction
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
Content-based MPEG video traffic modeling
IEEE Transactions on Multimedia
Dynamic resource allocation via video content and short-termtraffic statistics
IEEE Transactions on Multimedia
Traffic characteristics of H.264/AVC variable bit rate video
IEEE Communications Magazine
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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 50HD 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.