User Experience Measurement and Modeling for DASH Video
Ever since video compression techniques have been introduced, measurement of perceived video quality has been a non-trivial task. Recently, a new class of video transport techniques has been introduced for transmission of video over varying channels such as wireless network. These transport techniques, called adaptive streaming, vary the bit rate and quality of the transmitted video to match the available channel bandwidth. DASH, Dynamic Adaptive Streaming over HTTP, is a new worldwide standard for adaptive streaming of video, audio and other media such as closed captioning. The adaptive streaming techniques introduce an additional level of complexity for measuring perceived video quality, as it varies the video bitrate and quality. In this project, we study the perceived video quality using DASH. We investigate three parameters which impact user perceived video quality: initial delay, stall (frame freezing), and frame quality fluctuation. Moreover, for each parameter, we explore multiple dimensions that can have different effects on perceived quality. For example, in the case of the parameter stall, most previous research study how stall duration correlates with user perceived quality. On the other hand, besides stall duration, we also consider when the stalls happen and how the stalls are distributed, since we believe they will also greatly impact user experience. We will develop techniques to model the simultaneous effects of the multi-dimensional parameters on perceived video quality, leading to an AV-QoE (Adaptive Video-Quality of Experience) model, which can quantitatively measure user perceived quality. Extensive subjective tests are carried out to develop and evaluate this model. We will present results of the subjective tests, including preliminary results of how well the AV-QoE model correlates with human perception.