Cloud-assisted wireless networks are emerging solutions that unite wireless networks and cloud computing to deliver cloud services directly from the network edges to support the foreseen massive demands from data- and computation-hungry mobile users. In this chapter, we first provide an overview of the two emerging cloud-assisted wireless network paradigms – namely, the cloud radio access network (C-RAN), which aims at centralization of base station (BS) functionalities, and mobile-edge computing (MEC), which aims at providing the RAN with computing and storage resources. We then leverage the C-RAN and MEC paradigms to design novel cooperative caching frameworks that explore the synergies of the in-network computing and storage resources. Specifically, a novel cooperative hierarchical caching framework is designed in C-RAN, where caching is performed both at the distributed BSs and at the cloud processing unit (CPU), which bridges the gap between the traditional edge-based and core-based caching schemes. Furthermore, a joint cooperative caching and processing framework is designed in a MEC network, where the MEC servers perform both cache storage and video transcoding to support adaptive bitrate (ABR) video streaming.

This chapter focuses on the performance enhancement brought by the addition of caching capabilities to full-duplex (FD) radios in the context of ultra-dense networks (UDNs). In particular we aim at showing that the interference footprint of such networks, i.e., the major bottleneck to overcome to observe the theoretical FD throughput doubling at the network level, can be significantly reduced thanks to edge caching. A caching model is designed to mimic a geographical caching policy based on the popularity of local files and to compute their associated cache-hit probability. Subsequently, we calculate the probability of successful transmission of a file requested by a use equipment, either directly by its serving small cell base station (SCBS) or by the corresponding backhaul node (BN): this quantity is then used to lower-bound the throughput of the considered network. Our approach makes use of tools from stochastic geometryto guarantee the generality of our results and analytical tractability of the problem.

We study the delivery of 360°-navigable videos to 5G virtual reality (VR) wireless clients in future cooperative multi-cellular systems. A collection of small cell base stations interconnected via backhaul links are sharing their caching and computing resources to maximize the aggregate reward they earn by serving 360° videos requested by the wireless clients. We design an efficient representation method to construct the 360° videos such that they deliver only the remote scene viewpoint content genuinely needed by the VR users, thereby overcoming the present highly inefficient approach of sending a bulky 360° video, whose major part is made up of scene information never navigated by a user. Moreover, we design an optimization framework that allows the base stations to select cooperative caching/rendering/streaming strategies that maximize the aggregate reward they earn when serving the users for the given caching/computational resources at each base station.

In this chapter, we study the technoeconomic challenges for one of the most promising new caching paradigms, the elastic wireless edge caching solution, by which third parties dynamically lease storage resources in a wireless cloud. The main idea is the following: a mobile network operator (MNO) advertises storage prices for servers placed in proximity to the end users, and various content providers lease on-demand capacity to improve the quality of their services. We describe the main concepts and existing business models for the elastic CDN solution, provide an overview of the related work, and discuss the key differences between in-network and edge caching. We then present a detailed model for this system where the caches reside in cellular base stations. We formulate a problem where cache dimensioning, content caching, and request routing decisions are jointly optimized by a central processor (CP) to reduce content delivery delay, subject to a given leasing budget. We design a suite of dynamic solution algorithms, based on the Lyapunov drift-minus-benefit technique and present numerical experiments that quantify the benefits of elastic over typical static cache deployments

Wireless edge caching for mobile social networks (MSNs) has emerged as one of the prospective solutions to provide reliable and low-latency communication services for mobile users on social networking. In this chapter, we first give an overview of MSNs, including their development and challenges. We then discuss mobile edge caching (MEC) paradigms to address emerging issues for MSNs, e.g., service delay, users’ experience, and economic efficiency. In addition to the advantages, the development of MEC networks also places some key challenges’ such as hierarchical architecture of MEC networks, proactive caching, privacy, and security issues. The framework can authenticate MSN users based on public-key cryptography and predict their content demands utilizing a matrix factorization method. Based on the prediction, an optimal content caching policy for an MEC node is presented to minimize the average latency of all MSN users under the MEC nodes’ storage capacity constraints. Furthermore, this framework provides an optimal business model to maximize the revenue for MSN service providers based on the demands of the MSN users and the obtained optimal caching policy.

Large-scale data analysis is becoming an important source of information for mobile network operators (MNOs). MNOs can now investigate the feasibility of possible new technological advances such as storage/memory utilization, context awareness, and edge/cloud computing using analytic platforms designed for big data processing. Within this context, studying caching from a mobile data traffic analytical perspective can offer rich insights on evaluating the potential benefits and gains of proactive caching at base stations. In this chapter, we study how data collected from MNOs can be leveraged using machine learning tools in order to infer insights into the benefits of caching. Through our practical architecture, vast amount of data can be harnessed for content popularity estimations and placing strategic contents at base stations (BSs).Ourresults demonstrate several gains in terms of both content demand satisfaction and backhaul offloading rates while utilizing real-world data sets collected from a major MNO.

This chapter presents a content-centric framework for transmission optimization in cloud radio access networks (RANs) by leveraging wireless edge caching and physical-layer multicasting. We consider a cache-enabled cloud RAN, where each base station (BS) is equipped with a local cache and connected to a central processor (CP) via a backhaul link. The BSs acquire the requested contents either from their local caches or from the core network via the backhaul links. We first study the caching effects on multicast-enabled access downlink, where users requesting the same content are grouped together and served by the same BS or BS cluster using multicasting. We study the cache-aware joint design of the content-centric BS clustering and multicast beam-forming to minimize the system total power cost and backhaul cost subject to the quality-of-service (QoS) constraints for each multicast group.