Networking in a heterogeneous world: Optimization, measurement, and opportunities
Chen, Bo-Rong
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https://hdl.handle.net/2142/124500
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
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.
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