{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129680"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129680","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimizing rebuffering time under dynamic user behavior","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Zhu, Jiayu"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hu, Yih-Chun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-16","date_published":"2025-04-16","updated_at":"2026-07-22T22:25:05Z","subjects":["Video Streaming","QoE"],"languages":["en","eng"],"rights":["Copyright 2025 Jiayu Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129680","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hu, Yih-Chun"]},{"key":"dc:creator","label":"Author","values":["Zhu, Jiayu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-16","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Video Streaming","QoE"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Jiayu Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129680"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Jiayu Zhu, accepted the attached license on 2025-04-14 at 15:01.","The student, Jiayu Zhu, submitted this Thesis for approval on 2025-04-14 at 15:15.","This Thesis was approved for publication on 2025-04-16 at 10:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21713 on 2025-10-19 at 19:52:47","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Optimizing rebuffering time under dynamic user behavior"]}]}],"canonical_facts":{"dc:contributor":["Hu, Yih-Chun"],"dc:creator":["Zhu, Jiayu"],"dc:date":["2025-04-16","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Jiayu Zhu, accepted the attached license on 2025-04-14 at 15:01.","The student, Jiayu Zhu, submitted this Thesis for approval on 2025-04-14 at 15:15.","This Thesis was approved for publication on 2025-04-16 at 10:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21713 on 2025-10-19 at 19:52:47","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129680"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Jiayu Zhu"],"dc:subject":["Video Streaming","QoE"],"dc:title":["Optimizing rebuffering time under dynamic user behavior"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}