{"title":"Proactive retention-aware online video caching scheme in mobile edge computing","authors":"Guangzhou Liu, Zhen Qian, Guanghui Li","doi":"10.1016/j.comcom.2025.108313","DOIUrl":null,"url":null,"abstract":"<div><div>The current massive video requests have caused severe network congestion. To reduce transmission latency and improve user Quality of Experience (QoE), caching infrastructures are deployed closer to the edge. Nowadays, most caching systems tend to cache content with a high programming voltage to ensure a long retention time, which leads to significant cache damage. However, as new videos emerge every second, the rapidly changing popularity makes long retention time wasteful in terms of caching resource. Moreover, with the rise of emerging video formats (such as virtual reality content), the diverse requirements for transmission latency across various video categories make balancing user QoE more challenging. To tackle these challenges, we propose a joint optimization framework that balances user QoE and operational costs through video category recognition and adaptive retention time selection. First, we model user QoE as transmission latency cost and further formulate the optimization problem as a Markov Decision Process (MDP) to minimize the system cost. To solve the proposed problem, we design a two-step Double Deep Q-Network (DDQN)-based scheme. The scheme first determines the optimal retention time through unifying the process of action selection and state-value evaluation. Secondly, it makes replacement decisions according to the computed caching value of each content. By validating on three datasets, the experiments show that the proposed scheme outperforms the baseline algorithms in both cache hit rate and system cost.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"242 ","pages":"Article 108313"},"PeriodicalIF":4.3000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140366425002701","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The current massive video requests have caused severe network congestion. To reduce transmission latency and improve user Quality of Experience (QoE), caching infrastructures are deployed closer to the edge. Nowadays, most caching systems tend to cache content with a high programming voltage to ensure a long retention time, which leads to significant cache damage. However, as new videos emerge every second, the rapidly changing popularity makes long retention time wasteful in terms of caching resource. Moreover, with the rise of emerging video formats (such as virtual reality content), the diverse requirements for transmission latency across various video categories make balancing user QoE more challenging. To tackle these challenges, we propose a joint optimization framework that balances user QoE and operational costs through video category recognition and adaptive retention time selection. First, we model user QoE as transmission latency cost and further formulate the optimization problem as a Markov Decision Process (MDP) to minimize the system cost. To solve the proposed problem, we design a two-step Double Deep Q-Network (DDQN)-based scheme. The scheme first determines the optimal retention time through unifying the process of action selection and state-value evaluation. Secondly, it makes replacement decisions according to the computed caching value of each content. By validating on three datasets, the experiments show that the proposed scheme outperforms the baseline algorithms in both cache hit rate and system cost.
期刊介绍:
Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms.
Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.