题目：Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

主讲嘉宾：谢亚雄 助理教授 University at Buffalo, SUNY

主持人：崔来中 教授

时  间：2024年5月31日 10:00-11:30 AM

地  点：致腾楼623会议室

嘉宾简介

axiong Xie is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. Before joining UB, he was a postdoctoral researcher at Princeton University. He obtained his Ph.D. from Nanyang Technological University in Singapore. His research broadly focuses on mobile sensing and mobile networks, with specific emphasis on two areas: applying mobile sensing for smart health applications and building next-generation mobile networks to support diverse applications such as video streaming, video conferencing, and video analytics. His recent research has been published in top conferences, including SIGCOMM, MobiCom, NSDI, UbiComp, SenSys, and SigMetrics. In 2019, his work was selected for GetMobile research highlights.

报告摘要

hort 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 outperforms TikTok by 28-101%, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.