Proactive retention-aware online video caching scheme in mobile edge computing
Guangzhou Liu, Zhen Qian, Guanghui Li · Computer Communications · 2025
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.