HTTP Adaptive Streaming (HAS) is nowadays a popular solution for multimedia delivery. The novelty of HAS lies in the possibility of continuously adapting the streaming session to current network conditions, facilitated by Adaptive Bitrate (ABR) algorithms. Various popular streaming and Video on Demand services such as Netflix, Amazon Prime Video, and Twitch use this method. Given this broad consumer base, ABR algorithms continuously improve to increase user satisfaction. The insights for these improvements are, among others, gathered within the research area of Quality of Experience (QoE). Within this field, various researchers have dedicated their works to identifying potential impairments and testing their impact on viewers' QoE. Two frequently discussed visual impairments influencing QoE are stalling events and quality switches. So far, it is commonly assumed that those stalling events have the worst impact on QoE. This paper challenged this belief and reviewed this assumption by comparing stalling events with multiple quality and high amplitude quality switches. Two subjective studies were conducted. During the first subjective study, participants received a monetary incentive, while the second subjective study was carried out with volunteers. The statistical analysis demonstrated that stalling events do not result in the worst degradation of QoE. These findings suggest that a reevaluation of the effect of stalling events in QoE research is needed. Therefore, these findings may be used for further research and to improve current adaptation strategies in ABR algorithms.

翻译：HTTP自适应流媒体（HAS）如今已成为多媒体传输的主流方案。其创新性在于通过自适应比特率（ABR）算法，持续根据网络状况动态调整流媒体会话。Netflix、Amazon Prime Video、Twitch等主流流媒体及视频点播服务均采用该技术。面对如此庞大的用户群体，ABR算法持续优化以提升用户满意度，而体验质量（QoE）研究领域正是这些改进的重要来源。在该领域中，众多研究者致力于识别潜在损伤因素并评估其对观众QoE的影响。其中，播放中断事件和质量切换是两类频繁讨论的视觉损伤因素。现有研究普遍认为播放中断事件对QoE影响最为严重。本文质疑这一传统认知，通过对比播放中断事件与多种质量变化及高幅度质量切换，重新审视该假设。我们开展了两项主观实验：第一项实验对参与者给予现金奖励，第二项实验则采用志愿者形式。统计分析表明，播放中断事件并非导致QoE最严重退化的因素。这一发现提示我们需要重新评估播放中断事件在QoE研究中的影响权重，可为后续研究提供参考，并用于改进现有ABR算法的自适应策略。