Cooperative Caching Plan of Popular Videos for Mobile Users by Grouping Preferences

Abstract

Mobile traffic has grown fast in recent years, particularly for delivering popular video clips at anywhere. Based on a combination of Macro Cells and several Small Cells (SC) technologies, HetNets is gaining increasing attention due to the surge demand on high-quality mobile video services. To avoid bottleneck in the limited capacity of backhaul link, Mobile Edge Computing (MEC) is a promising solution, which computing and caching in the mobile edge in such a way that the buffered video can be delivered with less network latency and traffic load. Our goal is to build a cooperative caching plan for serving popular video clips over HetNets under the Joint Transmission (JT) method in MEC environment with least possible backhaul traffic. In the training phase, categorize similar users to clusters and to SCs using the well-known spectral clustering algorithm. Then aggregate the users' requests to be the request profile of the corresponding SCs. Third, share the caching space among cooperated SCs with the help of distributed LT codes. During the serving phase, new coming users will be assigned to appropriate SCs based on similarity between users and SCs. Our simulation results show that the backhaul traffic rate can decrease from 38% to 10% (or 62% to 19%) if cache space is acceptable.