Network-aware Prefetching Method for Short-Form Video Streaming
Duc Nguyen, Phong Nguyen, Vu Long, Truong Thu Huong, Pham Ngoc Nam

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.
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Taxonomy
TopicsImage and Video Quality Assessment · Multimedia Communication and Technology · Caching and Content Delivery
