Short video applications, with its attractive content, are becoming popular among users in recent years. In a typical short video application, a user may slide away the current video being watched and continue watching the next video. Such user interface causes significant bandwidth waste if users frequently slide a video away before finishing watching. Solutions to reduce bandwidth waste without impairing the Quality of Experience (QoE) are needed. Solving the problem requires adaptively prefetching of short video chunks, which is challenging as the download strategy needs to match unknown user viewing behavior and network conditions. In this paper, firstly, we formulate the problem of adaptive multi-video prefetching in short video streaming. Secondly, to facilitate the integration and comparison of researchers' algorithms, we design and implement a discrete-event simulator and release it as open source. Finally, based on the organization of the Short Video Streaming Grand Challenge at ACM Multimedia 2022, we analyze and summarize the algorithms of the contestants, with the hope of promoting the research community towards addressing this problem.
翻译:具有吸引力内容的短视频应用程序近年来在用户中越来越受欢迎。 在典型的短视频应用程序中,用户可能会将当前被监视的视频丢弃,并继续观看下一部视频。如果用户经常在观看结束前将视频丢弃,用户界面就会造成重大的带宽浪费。需要解决方案来减少带宽浪费,同时不影响经验的质量(QoE)。解决这个问题需要适应性地预先拉伸短视频块,因为下载战略需要匹配未知的用户观看行为和网络条件,这具有挑战性。在本文中,首先,我们提出适应性多视频预展的问题,在短视频流中进行。第二,促进研究人员算法的整合和比较,我们设计和实施一个离散活动模拟器,并将其作为开放来源予以发布。最后,根据2022年AM多媒体短视频短视频流大挑战的组织结构,我们分析和总结了参赛者的算法,希望推动研究界解决这一问题。