A Subjective and Objective Study of Stalling Events in Mobile Streaming Videos

Over-the-top mobile adaptive video streaming is invariably influenced by volatile network conditions, which can cause playback interruptions (stalling or rebuffering events) and bitrate fluctuations, thereby impairing users’ quality of experience (QoE). Video quality assessment models that can accurately predict users’ QoE under such volatile network conditions are rapidly gaining attention, since these methods could enable more efficient design of quality control protocols for media-driven services such as YouTube, Amazon, Netflix, and many others. However, the development of improved QoE prediction models requires datasets of videos afflicted with diverse stalling events that have been labeled with ground-truth subjective opinion scores. Towards this end, we have created a new mobile video quality database that we call LIVE Mobile Stall Video DatabaseII. Our database contains a total of 174 videos afflicted with distortions caused by 26 different stalling patterns. We describe the way we simulated the diverse stalling events to create a corpus of distorted videos, and we detail the human study we conducted to obtain continuous-time subjective scores from 54 subjects. We also present the outcomes of our comprehensive analysis of the impact of several factors that influence subjective quality of experience (QoE), and report the performance of existing QoEprediction models on our dataset. We are making the database (videos, subjective data, and video metadata) publicly available in order to help advance state-of-the-art research on user-centric mobile network planning and management. The database may be accessed at http://live.ece.utexas.edu/research/LIVEStallStudy/ liveMobile.html.