Quality of experience estimation for adaptive HTTP/TCP video streaming using H.264/AVC

Video services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users´ perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessment methods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a no-reference QoE monitoring module for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE.