Abstract
Mobile traffic has grown fast in recent years, particularly for delivering popular video clips. Based on a combination of Macro Cells and various Small Cell (SC) technologies, Hyper-dense Heterogeneous Networks (HetNets) is gaining increasing attention due to huge demand and popularity of mobile video traffic. To avoid bottleneck in the limited capacity of backhaul link to the core network, caching in the network edge in such a way that the buffered video can be delivered with less network latency and traffic load is very promising. A learning-based caching plan, on the basis of users’ preferences may not subject to change rapidly, is proposed in the thesis. Our goal is to build a distributed caching plan for serving popular video clips over HetNets with least possible backhaul traffic. In the learning phase of the proposed learning-based method, cluster and assign similar users to SCs using spectral clustering first. Second 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. However, the assignment of old users remains the same on the assumption that users’ preferences do not subject to change rapidly. Moreover, the clustering information can be applied to another application that several users can share downloading cooperatively if a group of smartphone users request watching the same video clips almost the same time, subsequent decreases the load of backhaul bandwidth.