4K video traffic prediction using seasonal autoregressive modeling

Abstract

-From the perspective of average viewer, high definition video streams such as HD (High Definition) and UHD (Ultra HD) are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding). Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

Video coding and constant quality evaluation using 4k aomenc-AV1 and rav1e-AV1 formats

“…About 43% improvement is reported for PSNR (Peak Signal-to-Noise ratio). In [16] 4k video traffic has been analyzed using prediction models, where the sequences were encoded using HEVC. In [17] the traffic variability has been R R R R 128x128 pixels R-recursive analyzed.…”