Poster: Quality of experience prediction of HTTP adaptive streaming traffic in LTE system

Abstract

With mobile video data traffic increasing, enhancing video quality of experience (QoE) has become an important issue. HTTP Adaptive Streaming (HAS) is a solution to give the user better experience by providing different video rates in changeable bandwidth. To infer the HAS QoE in LTE network, HAS engine we designed can produce video rates reflecting the viewer's subjective feeling adequately. Using the subjective score and the experimental parameters from HAS QoE evaluation system we planted, we predict the HAS QoE with evolved-PSNR (ePSNR) and predicted-MOS (pMOS) accurately in LTE network. Furthermore, we optimize the video rate with a better correlation.