{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/32994947"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/32994947","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Optimizing Placement and Delivery for Coded Caching","abstract":"In the new era immersed by massive information, the significant growth of internet traffic has placed unprecedented strain on modern communication networks. While advanced channel coding techniques offer chances to boost bandwidth efficiency, a widely adopted and highly effective approach to optimize traffic flows is from the edge device's perspective. By caching popular content at the network edge (such as base stations, routers, or end devices), frequently requested content can be delivered locally without traversing the entire network. This approach significantly reduces latency, smooth mainstream network load, and enhances the overall user experience during peak hours. While traditional caching methods have proven beneficial in practical, coded caching, built on a shared-link network, offers a novel approach that can yield substantially greater gains. This profound approach creates an extra global caching gain which scales with the aggregated cache size across the network. In this dissertation, we discuss subsequent models related to the coded caching network, and characterize the information-theorical bounds of the communication load. We first propose the general scheme of scalar linear function retrieval and address the feasibility constraints of are captured by the cycles of the universal graph. We then introduce the “hotplug” model — coded caching model with offline users, propose mechanisms to adapt existing schemes, extend the demand privacy against colluding users. We propose new schemes that achieve better performance, lower subpacketization, and exact optimality in certain memory regimes. Finally, we focus on the linear coding placement, propose a new scheme for linear broadcast computation by solving the linear programming problem via a subspace decomposition over representable polymatroid spaces. We derive the lower bound of optimal limits of the decentralized coded caching with random linear coding placement when the projection is exactly 1 file, which is achievable at most 3 users or certain conditions hold.","abstract_html":"In the new era immersed by massive information, the significant growth of internet traffic has placed unprecedented strain on modern communication networks. While advanced channel coding techniques offer chances to boost bandwidth efficiency, a widely adopted and highly effective approach to optimize traffic flows is from the edge device's perspective. By caching popular content at the network edge (such as base stations, routers, or end devices), frequently requested content can be delivered locally without traversing the entire network. This approach significantly reduces latency, smooth mainstream network load, and enhances the overall user experience during peak hours. While traditional caching methods have proven beneficial in practical, coded caching, built on a shared-link network, offers a novel approach that can yield substantially greater gains. This profound approach creates an extra global caching gain which scales with the aggregated cache size across the network. In this dissertation, we discuss subsequent models related to the coded caching network, and characterize the information-theorical bounds of the communication load. We first propose the general scheme of scalar linear function retrieval and address the feasibility constraints of are captured by the cycles of the universal graph. We then introduce the “hotplug” model — coded caching model with offline users, propose mechanisms to adapt existing schemes, extend the demand privacy against colluding users. We propose new schemes that achieve better performance, lower subpacketization, and exact optimality in certain memory regimes. Finally, we focus on the linear coding placement, propose a new scheme for linear broadcast computation by solving the linear programming problem via a subspace decomposition over representable polymatroid spaces. We derive the lower bound of optimal limits of the decentralized coded caching with random linear coding placement when the projection is exactly 1 file, which is achievable at most 3 users or certain conditions hold.","abstract_has_math":false,"creators":["Yinbin Ma (24399950)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T00:00:00Z","date_published":"2026-05-01T00:00:00Z","updated_at":"2026-07-27T21:33:45Z","subjects":["Coded Caching"],"languages":[],"rights":["In Copyright","Open Access after 2028-05-01"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.32994947.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Yinbin Ma (24399950)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Optimizing_Placement_and_Delivery_for_Coded_Caching/32994947"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Coded Caching"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright","Open Access after 2028-05-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.32994947.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the new era immersed by massive information, the significant growth of internet traffic has placed unprecedented strain on modern communication networks. While advanced channel coding techniques offer chances to boost bandwidth efficiency, a widely adopted and highly effective approach to optimize traffic flows is from the edge device's perspective. By caching popular content at the network edge (such as base stations, routers, or end devices), frequently requested content can be delivered locally without traversing the entire network. This approach significantly reduces latency, smooth mainstream network load, and enhances the overall user experience during peak hours. While traditional caching methods have proven beneficial in practical, coded caching, built on a shared-link network, offers a novel approach that can yield substantially greater gains. This profound approach creates an extra global caching gain which scales with the aggregated cache size across the network. In this dissertation, we discuss subsequent models related to the coded caching network, and characterize the information-theorical bounds of the communication load. We first propose the general scheme of scalar linear function retrieval and address the feasibility constraints of are captured by the cycles of the universal graph. We then introduce the “hotplug” model — coded caching model with offline users, propose mechanisms to adapt existing schemes, extend the demand privacy against colluding users. We propose new schemes that achieve better performance, lower subpacketization, and exact optimality in certain memory regimes. Finally, we focus on the linear coding placement, propose a new scheme for linear broadcast computation by solving the linear programming problem via a subspace decomposition over representable polymatroid spaces. We derive the lower bound of optimal limits of the decentralized coded caching with random linear coding placement when the projection is exactly 1 file, which is achievable at most 3 users or certain conditions hold."]},{"key":"dc:title","label":"Title","values":["Optimizing Placement and Delivery for Coded Caching"]}]}],"canonical_facts":{"dc:creator":["Yinbin Ma (24399950)"],"dc:date":["2026-05-01T00:00:00Z"],"dc:description":["In the new era immersed by massive information, the significant growth of internet traffic has placed unprecedented strain on modern communication networks. While advanced channel coding techniques offer chances to boost bandwidth efficiency, a widely adopted and highly effective approach to optimize traffic flows is from the edge device's perspective. By caching popular content at the network edge (such as base stations, routers, or end devices), frequently requested content can be delivered locally without traversing the entire network. This approach significantly reduces latency, smooth mainstream network load, and enhances the overall user experience during peak hours. While traditional caching methods have proven beneficial in practical, coded caching, built on a shared-link network, offers a novel approach that can yield substantially greater gains. This profound approach creates an extra global caching gain which scales with the aggregated cache size across the network. In this dissertation, we discuss subsequent models related to the coded caching network, and characterize the information-theorical bounds of the communication load. We first propose the general scheme of scalar linear function retrieval and address the feasibility constraints of are captured by the cycles of the universal graph. We then introduce the “hotplug” model — coded caching model with offline users, propose mechanisms to adapt existing schemes, extend the demand privacy against colluding users. We propose new schemes that achieve better performance, lower subpacketization, and exact optimality in certain memory regimes. Finally, we focus on the linear coding placement, propose a new scheme for linear broadcast computation by solving the linear programming problem via a subspace decomposition over representable polymatroid spaces. We derive the lower bound of optimal limits of the decentralized coded caching with random linear coding placement when the projection is exactly 1 file, which is achievable at most 3 users or certain conditions hold."],"dc:identifier":["10.25417/uic.32994947.v1"],"dc:relation":["https://figshare.com/articles/thesis/Optimizing_Placement_and_Delivery_for_Coded_Caching/32994947"],"dc:rights":["In Copyright","Open Access after 2028-05-01"],"dc:subject":["Coded Caching"],"dc:title":["Optimizing Placement and Delivery for Coded Caching"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T21:33:45Z"}