Objective Video Presentation QoE Predictor for Smart Adaptive Video Streaming

How to deliver videos to consumers over the network for optimal quality-of-experience (QoE) has been the central goal of modern video delivery services. Surprisingly, regardless of the large volume of videos being delivered everyday through various systems attempting to improve visual QoE, the actual QoE of end consumers is not properly assessed, not to say using QoE as the key factor in making critical decisions at the video hosting, network and receiving sites. Real-world video streaming systems typically use bitrate as the main video presentation quality indicator, but using the same bitrate to encode different video content could result in drastically different visual QoE, which is further affected by the display device and viewing condition of each individual consumer who receives the video. To correct this, we have to put QoE back to the driver’s seat and redesign the video delivery systems. To achieve this goal, a major challenge is to find an objective video presentation QoE predictor that is accurate, fast, easy-to-use, display device adaptive, and provides meaningful QoE predictions across resolution and content. We propose to use the newly developed SSIMplus index (https: //ece.uwaterloo.ca/~z70wang/research/ssimplus/) for this role. We demonstrate that based on SSIMplus, one can develop a smart adaptive video streaming strategy that leads to much smoother visual QoE impossible to achieve using existing adaptive bitrate video streaming approaches. Furthermore, SSIMplus finds many more applications, in live and file-based quality monitoring, in benchmarking video encoders and transcoders, and in guiding network resource allocations.