Distributed Video Content Caching Policy With Deep Learning Approaches for D2D Communication

In this paper, we develop a novel active video content caching scheme (RCC) based on a recommendation system, and consistent hash for device-to-device(D2D) communication. The RCC scheme is constructed by a cache placement scheme, a consistent hash algorithm, an optimal video segmentation scheme, and an optimal video library segmentation scheme. To begin with, a well-designed cache placement scheme based on a mobile model with helper notes, and video segmentation is proposed to reduce the redundancy of the videos, and save users' cache. In order to solve the interruption problem caused by segmentation, a consistent hash algorithm is introduced to improve the success probability of D2D communication. According to the recommendation system, all users' predictive score for all videos can be calculated, which results that users' most interesting video files rather than popular video files as previous work can be obtained, and cached in advance to improve the hitting probability. Furthermore, an optimal video segmentation scheme, and an optimal video library segmentation scheme are developed to minimize the transmission delay, and maximize the hitting probability respectively. Simulation results show that compared with other traditional caching schemes, the proposed RCC scheme can reach about 70% reduction in outage probability, 40% reduction in system latency, and 10% improvement in hitting probability, all of which can achieve the best performance.