VC-PPQ: Privacy-Preserving Q-Learning Based Video Caching Optimization in Mobile Edge Networks

Abstract

Mobile edge caching has great advantages in alleviating video traffic pressure and reducing transmission delay, which is considered as a hopeful solution to improve video resource utilization and achieve faster service response. However, due to insufficient and non-real time acquisition of the status between edge servers and mobile users, it is still difficult for existing solutions to improve the video quality of experience (VQoE) for mobile users. Besides, due to the mobility of users and malicious third-party attacks, users may suffer the risk of personal privacy (e.g. location, trajectory, preference, etc.) leakage when requesting video services that are scattered in different areas. In order to efficiently deal with these problems, this paper proposes a privacy-preserving Q-learning based video caching optimization framework in mobile edge networks (VC-PPQ), including a privacy scheme for user data and a dynamic video caching algorithm. Specifically, in the first task, the local differential privacy model is leveraged to protect the locations and preferences of users. Then, a data aggregation model is provided to make trade-off between aggregation accuracy and privacy protection. In the second task, we further obtain the video popularity in user's area by a computation service model, then formulate a caching objective function combined with transcoding technologies. Afterwards, Q-learning is adopted to obtain the goal of caching optimization, which can achieve low delay and high hit ratio. Finally, extensive simulations prove that our VC-PPQ framework delivers prominent performance advantages in terms of user privacy and caching services, which greatly improves the VQoE of users.