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）当前已成为多媒体传输的主流解决方案。其创新之处在于能够通过自适应比特率（ABR）算法，持续调整流媒体会话以适应网络状况。Netflix、Amazon Prime Video和Twitch等热门流媒体及视频点播服务均采用此方法。鉴于其广泛的用户基础，ABR算法不断改进以提升用户满意度。这些改进的洞见主要源于体验质量（QoE）领域的研究成果。在该领域，众多研究人员致力于识别潜在损伤并测试其对观众QoE的影响。两个频繁探讨的影响QoE的视觉损伤是卡顿事件与画质切换。目前普遍认为卡顿事件对QoE的影响最为严重。本文对这一传统认知提出质疑，通过将卡顿事件与多重画质切换及高振幅画质切换进行对比实验，重新审视了该假设。研究开展了两次主观实验：首次实验向参与者提供金钱激励，第二次实验则基于志愿者完成。统计分析表明，卡顿事件并非导致QoE最严重退化的因素。这一发现提示需要重新评估卡顿事件在QoE研究中的作用，可为后续研究及改进现有ABR算法中的自适应策略提供参考依据。