Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

TL;DR

Dashlet is a robust short video streaming system that uses swipe pattern insights to optimize pre-buffering, significantly improving user experience and reducing wasted bandwidth compared to existing platforms.

Contribution

The paper introduces Dashlet, a novel pre-buffering mechanism based on swipe statistics, enhancing QoE in short video streaming without machine learning.

Findings

Achieves 77-99% of oracle QoE

Outperforms TikTok by 43.9-45.1x in QoE

Reduces 30% of wasted downloaded bytes

Abstract

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.