User Experience Modeling for DASH Video

Abstract

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 bit rate and quality. In this paper, we study the perceived video quality using DASH. We investigate three factors which impact user perceived video quality: initial delay, stall (frame freezing), and bit rate (frame quality) fluctuation. Moreover, for each factor, we explore multiple dimensions that can have different effects on perceived quality. For example, in the case of the factor stall, while most previous research have studied how stall duration correlates with user perceived quality, we also consider when the stalls happen and how the stalls are distributed, since we believe they may also impact user experience. We design and conduct extensive subjective tests to study the impairments of the different dimensions of the three factors on user perceived video quality. We will describe the methodology to design the subjective tests, and present the results of the subjective tests. Based on the subjective tests, we derive impairment functions which can quantitatively measure the impairment of each factor on the user experience of any DASH video, and also provide validation results.