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