4K video traffic analysis using seasonal autoregressive model for traffic prediction

High definition video streams increase their Internet presence year over year. Therefore, they are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. In this paper, we apply a seasonal autoregressive model for modeling and prediction of 4K video traffic, encoded with H.265 (HEVC) encoding standard. The obtained experimental results are based on analyzing over 17.000 high definition video frames. We show that the proposed methodology provides good accuracy in high definition video traffic modeling and afterwards we gave an overview of pros and cons of 4K video traffic prediction.