Optimizing rebuffering time under dynamic user behavior

Zhu, Jiayu

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https://hdl.handle.net/2142/129680 Copy

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

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129680

Copyright and License Information

Copyright 2025 Jiayu Zhu

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