Modeling and Prediction of High Defninition Video Traffic: A Real-World Case Study

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 comparing over 50 HD video traces. We show through our results that our model: simplified seasonal ARIMA (SAM) provides a good representation for HD videos, and it provides significant improvements in prediction accuracy over other regressions methods. In addition, we discuss our methodology to collect and encode our library of HD video traces. We describe the tools that we have created and used in generating create and analyzing these traces. We have made these tools, along with our large collection of HD video traces, available for the research community. We illustrate the simplicity of our approach and we discuss the importance of our modeling and prediction method and its impact on other areas of study.