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  • Open Access

    ARTICLE

    Utility-Driven Edge Caching Optimization with Deep Reinforcement Learning under Uncertain Content Popularity

    Mingoo Kwon, Kyeongmin Kim, Minseok Song*

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 519-537, 2025, DOI:10.32604/cmc.2025.066754 - 29 August 2025

    Abstract Efficient edge caching is essential for maximizing utility in video streaming systems, especially under constraints such as limited storage capacity and dynamically fluctuating content popularity. Utility, defined as the benefit obtained per unit of cache bandwidth usage, degrades when static or greedy caching strategies fail to adapt to changing demand patterns. To address this, we propose a deep reinforcement learning (DRL)-based caching framework built upon the proximal policy optimization (PPO) algorithm. Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing More >

  • Open Access

    ARTICLE

    Dynamic Vehicular Clustering Enhancing Video on Demand Services Over Vehicular Ad-hoc Networks

    M. Almutiq1, L. Sellami1,2,*, B. Alaya1,3

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3493-3510, 2022, DOI:10.32604/cmc.2022.024571 - 29 March 2022

    Abstract Nowadays, video streaming applications are becoming one of the tendencies driving vehicular network users. In this work, considering the unpredictable vehicle density, the unexpected acceleration or deceleration of the different vehicles included in the vehicular traffic load, and the limited radio range of the employed communication scheme, we introduce the “Dynamic Vehicular Clustering” (DVC) algorithm as a new scheme for video streaming systems over vehicular ad-hoc networks (VANET). The proposed algorithm takes advantage of the small cells concept and the introduction of wireless backhauls, inspired by the different features and the performance of the Long… More >

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