Networking in a heterogeneous world: Optimization, measurement, and opportunities

Chen, Bo-Rong

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https://hdl.handle.net/2142/124500 Copy

Description

Title

Networking in a heterogeneous world: Optimization, measurement, and opportunities

Author(s)

Chen, Bo-Rong

Issue Date

2024-04-08

Director of Research (if dissertation) or Advisor (if thesis)

Hu, Yih-Chun

Doctoral Committee Chair(s)

Hu, Yih-Chun

Committee Member(s)

• Bailey, Michael
• Godfrey, Brighten
• Mittal, Radhika

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Internet Bandwidth Management
• Content Delivery
• End-to-end User Experience
• Remote Traffic Shaping
• Co-bottleneck Detection
• Audience Retention Curve
• Youtube
• Bilibili

Language

eng

Abstract

Over the past decade, research on video streaming systems has often relied on simplified user studies that fail to capture the diversity and complexity of real-world user behavior, who contribute different economic and social values to content providers. This study seeks to address this oversight by developing and evaluating video streaming systems that aim to enhance provider revenues and take into account the dynamic behaviors of users. First of all, content providers face challenges with delivering content due to bandwidth constraints beyond their control. In this study, we introduce FlowTele, a novel system for optimizing internet traffic through remote traffic shaping, diverging from traditional Transmission Control Protocol (TCP) fairness. FlowTele, designed without the need for in-network or special client support, has shown to enhance providers' revenues by 20%--30% through strategic bandwidth reallocation in various network conditions. Additionally, the research investigates Quality of Experience (QoE) fairness among other metrics that content providers can optimize with FlowTele, offering a solution to bypass bandwidth bottlenecks while maintaining TCP-friendly operations. Secondly, to enhance video streaming quality, adaptive bitrate streaming (ABR) techniques and QoE metrics are utilized. Traditional methods often ignore user interactions like seeking or replaying, leading to a gap in understanding streaming quality. To fill this gap, we collected and analyzed 229,178 audience retention curves from YouTube and Bilibili, which we called vRetention, revealing significant variations in user engagement across different content types and regions. This data aids in refining ABR designs and QoE assessments, offering a deeper insight into user behavior for improved streaming experiences. Finally, evaluating the real-world QoE for ABR is difficult due to traditional evaluations overlooking dynamic user behaviors (e.g., seeking or abandoning videos). This research addresses this by analyzing audience retention curve data and developing mRetention, a dynamic behavior model to assess ABR performance. Findings indicate significant QoE degradation under dynamic behaviors, highlighting issues like overbuffering across network types and insufficient consideration for varied playback speeds.

Graduation Semester

2024-05

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/124500

Copyright and License Information

Copyright 2024 Bo-Rong Chen

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