The evolution of broadband networks, including 5G, will enable streaming of high-definition video (UHD, 4k, and 8k), and evaluating the quality of experience is paramount for service providers who must ensure high QoE with appropriate resource allocation to avoid user churn. On-demand video services (YouTube, Netflix, etc.) typically use adaptive video streaming based on HTTP (HAS), and the focus of this research is on MPEG DASH, the latest standard for HAS. The goal of this research is to develop metrics for QoE estimation in HAS systems using video sequence complexity parameters in addition to standard video streaming parameters (initial delay, stalling, and quality change parameters).
