论文标题
dashlet：可驯服不确定性的不确定性，用于鲁棒的短视频流
Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming
论文作者
论文摘要
简短的视频流应用程序最近获得了大量的吸引力，但是他们从根本上刷新用户提供的非线性视频演示会改变最大化用户在网络吞吐量和用户滑动时机的变化时最大化用户的体验质量。本文介绍了Dashlet的设计和实现，Dashlet是一种针对短视频流应用程序中高质量的体验量身定制的系统。通过洞察力，我们从一项野外Tiktok性能研究和一项针对滑动模式的用户研究中汲取了一种，Dashlet提出了一种新颖的替代视频块预防预防机制，该机制利用了一种简单，非机器学习的基于机器学习的模型，以确定前延误订单和比特级和比特级。最终结果是一个实现Oracle系统QOE的77-99％的系统，并以43.9-45.1x的速度优于Tiktok，而从未观看的下载视频中浪费的字节数量也减少了30％。
Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that achieves 77-99% of an oracle system's QoE and outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.
