Mobile Edge Computing (MEC) is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile Radio Access Network (RAN). MEC servers are deployed on generic computing platform within the RAN and allow for delay-sensitive and context-aware applications to be executed in close proximity to the end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, highbandwidth, and agile mobile services. This article envisages a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and that amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use-cases ranging from mobile-edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open-research issues that need to be addressed in order to make an efficient integration of MEC into 5G ecosystem.

Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network condition. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NPcompleteness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.Index Terms-Collaborative caching; adaptive bitrate streaming; multi-bitrate video; mobile-edge computing; joint caching and processing.

Over the last few years, Cloud Radio Access Network (C-RAN) is proposed as a transformative architecture for 5G cellular networks that brings the flexibility and agility of cloud computing to wireless communications. At the same time, content caching in wireless networks has become an essential solution to lower the content-access latency and backhaul traffic loading, leading into user Quality of Experience (QoE) improvement and network cost reduction. In this article, a novel Cooperative Hierarchical Caching (CHC) framework in C-RAN is introduced where contents are jointly cached at the BaseBand Unit (BBU) and at the Radio Remote Heads (RRHs). Unlike in traditional approaches, the cache at the BBU, cloud cache, presents a new layer in the cache hierarchy, bridging the latency/capacity gap between the traditional edge-based and core-based caching schemes. Trace-driven simulations reveal that CHC yields up to 51% improvement in cache hit ratio, 11% decrease in average content-access latency, and 18% reduction in backhaul traffic load compared to the edge-only caching scheme with the same total cache capacity. Before closing the article, we discuss the key challenges and promising opportunities for deploying content caching in C-RAN in order to make it an enabler technology in 5G ultra-dense systems.