Optimizing rebuffering time under dynamic user behavior
Zhu, Jiayu
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Permalink
https://hdl.handle.net/2142/129680
Description
Title
Optimizing rebuffering time under dynamic user behavior
Author(s)
Zhu, Jiayu
Issue Date
2025-04-16
Director of Research (if dissertation) or Advisor (if thesis)
Hu, Yih-Chun
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Video Streaming
QoE
Language
eng
Abstract
Adaptive bitrate streaming (ABR) and quality of experience (QoE) metrics are proposed to enhance video streaming quality across various Internet connections. Traditional approaches to evaluating these metrics often ignore common user behaviors like seeking, jumping, or replaying video segments, leading to gaps in QoE understanding. Addressing this, we first collected thousands of audience retention curves from Bilibili, offering a thorough view of viewer engagement and diverse watching styles, to prove that the audience does not watch a video in full. Our analysis also reveals notable behavioral differences across video categories, with Bilibili showing trends of early video abandonment, possibly influenced by platform-specific factors and shorter video formats. This enhanced grasp of user engagement aids in refining ABR and QoE metrics. To address the QoE reduction due to the nature of dynamic use behavior, we thus propose StallFreeSeek streaming system, which utilizes the good network conditions given by increased deployment of fiber-to-the-home and 5G services, as CDN appliances inside of ISPs drive down round-trip time. The intuition behind StallFreeSeek (SFS) is to prefetch small chunks densely distributed across the video, allowing immediate playback on almost any skip, and exploit strong network performance to fetch ever-larger chunks before each previous chunk finishes playback. Our evaluations show that SFS improves Quality-of-Experience and stall times in suitable network conditions while wasting less buffered content, and never performs worse than dash.js across thousands of runs. Our evaluations show that across video genres, models of user seeks, and in real-world user studies, SFS is never inferior to dash.js in QoE, stall time, or buffer waste, and when network conditions allow, performs significantly better.
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