Network-aware Prefetching Method for Short-Form Video Streaming

TL;DR

This paper introduces a network-aware prefetching method tailored for short-form video streaming platforms, which dynamically adjusts data preloading based on network and user behavior to reduce waste.

Contribution

It presents a novel, resource-efficient prefetching approach that adapts to network and user behavior, significantly reducing data waste in short-form video streaming.

Findings

Reduces data waste by 37-52% compared to existing methods

Adapts prefetching based on network throughput and user viewing patterns

Improves streaming efficiency for short-form videos

Abstract

Recent years have witnessed the rising of short-form video platforms such as TikTok. Apart from conventional videos, short-form videos are much shorter and users frequently change the content to watch. Thus, it is crucial to have an effective streaming method for this new type of video. In this paper, we propose a resource-efficient prefetching method for short-form video streaming. Taking into account network throughput conditions and user viewing behaviors, the proposed method dynamically adapts the amount of prefetched video data. Experiment results show that our method can reduce the data waste by 37~52% compared to other existing methods.