The surge of mobile data traffic forces network operators to cope with capacity shortage. The deployment of small cells in 5G networks is meant to reduce latency, backhaul traffic and increase radio access capacity. In this context, mobile edge computing technology will be used to manage dedicated cache space in the radio access network. Thus, mobile network operators will be able to provision OTT content providers with new caching services to enhance the quality of experience of their customers on the move. In turn, the cache memory in the mobile edge network will become a shared resource. Hence, we study a competitive caching scheme where contents are stored at given price set by the mobile network operator. We first formulate a resource allocation problem for a tagged content provider seeking to minimize the expected missed cache rate. The optimal caching policy is derived accounting for popularity and availability of contents, the spatial distribution of small cells, and the caching strategies of competing content providers. It is showed to induce a specific order on contents to be cached based on their popularity and availability. Next, we study a game among content providers in the form of a generalized Kelly mechanism with bounded strategy sets and heterogeneous players. Existence and uniqueness of the Nash equilibrium are proved. Finally, extensive numerical results validate and characterize the performance of the model.
A heterogenous network with base stations (BSs), small base stations (SBSs), and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an @math difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as @math , where @math is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of @math and scales linearly with the dimension of the distribution parameter. We investigate the problem of optimal request routing and content caching in a heterogeneous network supporting in-network content caching with the goal of minimizing average content access delay. Here, content can either be accessed directly from a back-end server (where content resides permanently) or be obtained from one of multiple in-network caches. To access a piece of content, a user must decide whether to route its request to a cache or to the back-end server. Additionally, caches must decide which content to cache. We investigate the problem complexity of two problem formulations, where the direct path to the back-end server is modeled as i) a congestion-sensitive or ii) a congestion-insensitive path, reflecting whether or not the delay of the uncached path to the back-end server depends on the user request load, respectively. We show that the problem is NP-complete in both cases. We prove that under the congestion-insensitive model the problem can be solved optimally in polynomial time if each piece of content is requested by only one user, or when there are at most two caches in the network. We also identify a structural property of the user-cache graph that potentially makes the problem NP-complete. For the congestion-sensitive model, we prove that the problem remains NP-complete even if there is only one cache in the network and each content is requested by only one user. We show that approximate solutions can be found for both models within a (1-1 e) factor of the optimal solution, and demonstrate a greedy algorithm that is found to be within 1 of optimal for small problem sizes. Through trace-driven simulations we evaluate the performance of our greedy algorithms, which show up to a 50 reduction in average delay over solutions based on LRU content caching. Anticipating multimedia file requests via caching at the small cell base stations (SBSs) of a cellular network has emerged as a promising technique for optimizing the quality of service (QoS) of wireless user equipments (UEs). However, developing efficient caching strategies must properly account for specific small cell constraints, such as backhaul congestion and limited storage capacity. In this paper, we address the problem of devising a usercell association, in which the SBSs exploit caching capabilities to overcome the backhaul capacity limitations and enhance the users’ QoS. In the proposed approach, the SBSs individually decide on which UEs to service based on both content availability and on the data rates they can deliver, given the interference and backhaul capacity limitations. We formulate the problem as a oneto-many matching game between SBSs and UEs. To solve this game, we propose a distributed algorithm, based on the deferred acceptance scheme, that enables the players (i.e., UEs and SBSs) to self-organize into a stable matching, in a reasonable number of algorithm iterations. Simulation results show that the proposed cell association scheme yields significant gains, reaching up to 21 improvement compared to a traditional cell association techniques with no caching considerations. We consider a wireless Device-to-Device (D2D) network where communication is restricted to be single-hop, users make arbitrary requests from a finite library of possible files and user devices cache information in the form of linear combinations of packets from the files in the library (coded caching). We consider the combined effect of coding in the caching and delivery phases, achieving "coded multicast gain", and of spatial reuse due to local short-range D2D communication. Somewhat counterintuitively, we show that the coded multicast gain and the spatial reuse gain do not cumulate, in terms of the throughput scaling laws. In particular, the spatial reuse gain shown in our previous work on uncoded random caching and the coded multicast gain shown in this paper yield the same scaling laws behavior, but no further scaling law gain can be achieved by using both coded caching and D2D spatial reuse.