Mobile and High Definition Video Streams
Analysis, Modeling, Generation, and Dynamic Resource Allocation
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The importance of video streaming management has been emphasized with the advent of High Definition (HD) video streaming, as it requires by its nature more network resources. In this book, we discuss several contributions towards advancing mobile video streaming research. We present a general, simple and accurate video source model: Simplified Seasonal ARIMA Model (SAM). SAM is capable of modeling video traces encoded with MPEG-4 Part2/Part10, and Scalable Video Codec (SVC) standards, using different encoding settings. We also detail our thorough analyses of our large collection of HD video tr...
The importance of video streaming management has been emphasized with the advent of High Definition (HD) video streaming, as it requires by its nature more network resources. In this book, we discuss several contributions towards advancing mobile video streaming research. We present a general, simple and accurate video source model: Simplified Seasonal ARIMA Model (SAM). SAM is capable of modeling video traces encoded with MPEG-4 Part2/Part10, and Scalable Video Codec (SVC) standards, using different encoding settings. We also detail our thorough analyses of our large collection of HD video traces. These analyses include: a full statistical analysis, in addition to modeling, factor and cluster analyses. Our results show that by using SAM, we can achieve significant improvements in video traffic prediction accuracy. In addition, we discuss several developed video tools, including a SAM-based HD video traffic generator. We also discuss a SAM-based delay-guaranteed dynamic resource allocation (DRA) scheme that can provide significant improvement in bandwidth utilization. These discussed analyses should be especially useful to mobile, wireless and video traffic researchers.
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