{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129940"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129940","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Context-aware spatiotemporal reconstruction for loss-resilient video offloading under timing constraints","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","abstract_has_math":false,"creators":["Li, John"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Nahrstedt, Klara"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-07-18","date_published":"2025-07-18","updated_at":"2026-07-22T22:25:06Z","subjects":["Iot","Video Offloading"],"languages":["en","eng"],"rights":["Copyright 2025 John Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129940","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nahrstedt, Klara"]},{"key":"dc:creator","label":"Author","values":["Li, John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-18","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Iot","Video Offloading"]}]},{"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 John Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129940"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, John Li, accepted the attached license on 2025-07-14 at 11:49.","The student, John Li, submitted this Thesis for approval on 2025-07-14 at 16:27.","This Thesis was approved for publication on 2025-07-18 at 13:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22546 on 2025-10-20 at 20:15:13","The deployment of IoT devices for video offloading is rapidly expanding, driven by the growing incentive for efficient visual data processing at the edge. However, as these IoT video systems scale, strict real-time constraints and limited, variable network bandwidth can undermine the reliability of video transmission. To contend with such latency budgets and unpredictable network conditions, video frames that arrive late are typically discarded at the application layer. These losses—often occurring even without network-layer drops or corruption—can drastically degrade Quality of Experience (QoE). Conventional video codecs provide little protection against such loss, while loss-resilient solutions such as packet retransmission and Forward Error Correction (FEC) struggle to operate effectively under increasingly severe losses. This becomes especially evident when subject to more stringent timing constraints and volatile network bandwidth. This thesis presents CASTR, a context-aware spatiotemporal reconstruction framework designed for loss-resilient, adaptive video offloading. CASTR consists of a progressive encoder to transmit essential features first, increasing the likelihood that the most important semantic information arrives before their real-time deadlines. Next, CASTR employs a convolutional Long Short-Term Memory (ConvLSTM) that leverages spatiotemporal context from neighboring frames to impute missing features and mitigate the effects of data loss. A decoder then reconstructs the features back into video frames. Experiments show that CASTR generally degrades more gracefully across a broad spectrum of packet loss scenarios compared to prior neural baselines. Furthermore, CASTR maintains perceptually reasonable video quality even under very severe loss rates (up to 90%+), demonstrating CASTR’s potential for robust video offloading in time-sensitive and network-constrained settings."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Context-aware spatiotemporal reconstruction for loss-resilient video offloading under timing constraints"]}]}],"canonical_facts":{"dc:contributor":["Nahrstedt, Klara"],"dc:creator":["Li, John"],"dc:date":["2025-07-18","2025-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-20 without embargo terms","The student, John Li, accepted the attached license on 2025-07-14 at 11:49.","The student, John Li, submitted this Thesis for approval on 2025-07-14 at 16:27.","This Thesis was approved for publication on 2025-07-18 at 13:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22546 on 2025-10-20 at 20:15:13","The deployment of IoT devices for video offloading is rapidly expanding, driven by the growing incentive for efficient visual data processing at the edge. However, as these IoT video systems scale, strict real-time constraints and limited, variable network bandwidth can undermine the reliability of video transmission. To contend with such latency budgets and unpredictable network conditions, video frames that arrive late are typically discarded at the application layer. These losses—often occurring even without network-layer drops or corruption—can drastically degrade Quality of Experience (QoE). Conventional video codecs provide little protection against such loss, while loss-resilient solutions such as packet retransmission and Forward Error Correction (FEC) struggle to operate effectively under increasingly severe losses. This becomes especially evident when subject to more stringent timing constraints and volatile network bandwidth. This thesis presents CASTR, a context-aware spatiotemporal reconstruction framework designed for loss-resilient, adaptive video offloading. CASTR consists of a progressive encoder to transmit essential features first, increasing the likelihood that the most important semantic information arrives before their real-time deadlines. Next, CASTR employs a convolutional Long Short-Term Memory (ConvLSTM) that leverages spatiotemporal context from neighboring frames to impute missing features and mitigate the effects of data loss. A decoder then reconstructs the features back into video frames. Experiments show that CASTR generally degrades more gracefully across a broad spectrum of packet loss scenarios compared to prior neural baselines. Furthermore, CASTR maintains perceptually reasonable video quality even under very severe loss rates (up to 90%+), demonstrating CASTR’s potential for robust video offloading in time-sensitive and network-constrained settings."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129940"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 John Li"],"dc:subject":["Iot","Video Offloading"],"dc:title":["Context-aware spatiotemporal reconstruction for loss-resilient video offloading under timing constraints"],"dc:type":["text"],"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":"2026-07-22T22:25:06Z"}