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. 1. Introduction Web-based video streaming websites facilitate the creation and distribution of digital video contents to millions of people. Websites like YouTube [1] are now considered to be among the most accessed websites by Internet users. Such websites are now accounting for 27 percent of the Internet traffic, rising from 13 percent in one year [2]. Internet video traffic is expected to amount to 50% of consumer Internet traffic in 2012 [3]. This surge in traffic percentage can be explained by the latest surveys that show that the percentage of US Internet users watching streaming videos has increased from 81% to 84.4%, and the average time spent per month increased from 8.3 to 10.8 hours/month in just three months period July–October of 2009 [4, 5]. Additionally, several websites, for example, Hulu [6] and Netflix [7], have started offering access to TV shows and selected movies that has increased the reliance of the daily Internet users on such websites and augmented their expectations of the level of services and quality of delivery. Resource and bandwidth allocation schemes for video streaming are dependent on their ability to predict and manage the time variant demand of video streams. Existing dynamic resource allocation schemes [8–10] utilize video traffic prediction to offer better accommodation for existing video traffic, and allow higher admission rates. The traffic predictor is the most important part in dynamic bandwidth allocation. It is can be based either on traffic characteristics or on the video
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