Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an @math filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.
Mobile user's capacity is greatly augmented in the era of MEC. As a result, mobile service provisioning is expected to have further improved quality of experience (QoE) @cite_21 . To this end, various mobile edge architectures have been proposed. Tandom proposed to deploy edge resources within radio access networks. They characterized the relationship between latency and caching size, as well as latency and fronthaul capacity, from an information-theoretic perspective @cite_12 . Yang introduced an edge resource provisioning architecture based on cloud radio access network (C-RAN), and devised a cloud-edge interoperation scheme via software defined networking techniques @cite_16 . Tong designed a hierarchical edge architecture, aiming at making efficient use of edge resources when serving the peak loads from mobile users @cite_1 . As the 5G wireless network is expected to incorporate diverse access technologies, in this paper, we consider edge caching in the context of heterogeneous networks. Potential EN deployment can be capacity-augmented base stations, WiFi access points and other devices with excess resources.
@cite_21 @cite_1 @cite_12 @cite_16
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In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes. Mobile Edge Computing (MEC) plays a key role in the 5G roadmap, as a way to bring information technology (IT) services closer to the mobile users by empowering radio access points with additional functionalities, like caching or computation offloading capabilities. At the physical layer, some of the key technologies enabling very low latency mobile services are dense deployment of radio access points, massive MIMO and millimeter-wave (mmW) communications for radio access as well as for radio fronthaul backhaul. In this paper, we merge computation offloading techniques for mobile edge computing with mmW communications and show how the joint optimization of computation communication resources is crucial to design an energy efficient mobile edge computing system. In particular, we tackle the intermittency of mmW links by considering multi-link communications and show how to take advantage of preliminary estimation of blocking probabilities. While 5G wireless networks are expected to handle ever growing data avalanche, classical deployment optimization approaches such as hyper- dense deployment of base stations or having more bandwidth are cost-inefficient, therefore seen as stopgaps. In this regard, context-aware approaches which exploits human predictability, recent advances in storage, edge cloud computing and big data analytics are needed. In this article, we approach to this problem from a proactive caching perspective where gains of cache-enabled base stations in 5G wireless are studied. In particular, huge amount of real data from a telecom operator in Turkey is collected processed on a big data platform, and an analysis is carried out for content popularity estimation for caching, aiming to improve users' experience in terms of request satisfactions and offload the backhaul. Subsequently, with this mobile traffic data collected from many base stations within several hours of time interval and estimation of content popularity via machine learning tools, we investigate the gains of the proactive caching via numerical simulations. The results show that proactive caching fulfils 100 of user request satisfaction and offloads 98 of the backhaul, in a setting of 16 base station with 15.4 Gbyte of storage size (87 of the total catalog size) and 10 of content ratings. Edge caching and the centralization of baseband processing by means of the C-RAN architecture are among the most promising and transformative trends in the evolution of wireless networks. A key advantage of C-RAN is the possibility to perform cooperative transmission across multiple edge nodes, such as small cell base stations, thanks to centralized cloud processing. Cloud processing, however, comes at the cost of the potentially large delay entailed by fronthaul transmission between edge and cloud. In contrast, edge caching enables the low-latency transmission of popular multimedia content, but at the cost of constraining the operation of the edge nodes to decentralized transmission strategies with limited interference management capabilities. In order to accommodate the broad range of quality of service requirements of mobile broadband communication, in terms of spectral efficiency and latency, that are envisioned to be within the scope of 5G systems and beyond, this article considers a hybrid architecture, referred to as fog RAN (F-RAN), that harnesses the benefits of, and the synergies between, edge caching and C-RAN. In an F-RAN, edge nodes may be endowed with caching capabilities, while at the same time being controllable from a central cloud processor as in a C-RAN. In this article, an information-theoretic framework is presented that aims to characterize the main trade-offs between performance of an F-RAN, in terms of worst case delivery latency, and its resources: caching and fronthaul capacities.