High definition video streams are gaining larger shares of the Internet usage for typical users on daily basis. This is an expected result of the current boom in the online standard and high definition (HD) video streaming services such as YouTube and Hulu. Because of these video streams' unique statistical characteristics and their high bandwidth requirements, they are considered to be a continuous challenge in both network scheduling and resource allocation fields. In this paper we provide a statistical analysis of over 50 high definition video traces that resembles wide varieties of high definition video traffic workloads. We performed both factor and cluster analysis on our collection of video traces to support a better understanding of video stream workload characteristics and their impact on network traffic. Additionally, we compare and evaluate different modeling approaches for high definition videos traces.
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