Bandwidth-Efficient Multi-video Prefetching for Short Video Streaming

TL;DR

This paper addresses bandwidth waste in short video streaming by formulating an adaptive prefetching problem, providing a simulator for research, and analyzing competing algorithms to improve user experience and efficiency.

Contribution

It introduces a formal problem definition for adaptive multi-video prefetching, develops an open-source simulator, and analyzes algorithms from a recent challenge to guide future research.

Findings

Formulated the adaptive prefetching problem for short videos.

Developed and released an open-source discrete-event simulator.

Analyzed algorithms from the ACM Multimedia 2022 challenge.

Abstract

Applications that allow sharing of user-created short videos exploded in popularity in recent years. A typical short video application allows a user to swipe away the current video being watched and start watching the next video in a video queue. Such user interface causes significant bandwidth waste if users frequently swipe 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 our work, we first formulate the problem of adaptive multi-video prefetching in short video streaming. Then, to facilitate the integration and comparison of researchers' algorithms towards solving the problem, we design and implement a discrete-event simulator, which we release 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.