2022/04/27 by Zhuqi Li, Yaxiong Xie, Li, Zhuqi +5 · 2 citations
Computer Science · Social Sciences · #FOS: Computer and information sciences #Image and Video Quality Assessment #Multimedia Communication and Technology #Networking and Internet Architecture (cs.NI) #Video Coding and Compression Technologies
paper · pdf · doi:10.48550/arxiv.2204.12954
openalex publication_date 2022/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.