Perceptual Quality Of Http Adaptive Streaming Strategies: Cross-Experimental Analysis Of Multi-Laboratory And Crowdsourced Subjective Studies

Today's packet-switched networks are subject to bandwidth fluctuations that cause degradation of the user experience of multimedia services. In order to cope with this problem, HTTP adaptive streaming (HAS) has been proposed in recent years as a video delivery solution for the future Internet and being adopted by an increasing number of streaming services, such as Netflix and Youtube. HAS enables service providers to improve users' quality of experience (QoE) and network resource utilization by adapting the quality of the video stream to the current network conditions. However, the resulting time-varying video quality caused by adaptation introduces a new type of impairment and thus novel QoE research challenges. Despite various recent attempts to investigate these challenges, many fundamental questions regarding HAS perceptual performance are still open. In this paper, the QoE impact of different technical adaptation parameters, including chunk length, switching amplitude, switching frequency, and temporal recency, are investigated. In addition, the influence of content on perceptual quality of these parameters is analyzed. To this end, a large number of adaptation scenarios have been subjectively evaluated in four laboratory experiments and one crowdsourcing study. A statistical analysis of the combined data set reveals results that partly contradict widely held assumptions and provide novel insights in perceptual quality of adapted video sequences, e.g., interaction effects between quality switching direction (up/down) and switching strategy (smooth/abrupt). The large variety of experimental configurations across different studies ensures the consistency and external validity of the presented results that can be utilized for enhancing the perceptual performance of adaptive streaming services.