Multimedia content delivery through the cellular infrastructure increases fast due to the enormous volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests put a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple Knapsack (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.

Optimal caching strategies of popular contents in heterogeneous cellular networks are studied. The increasing demand for data traffic by users of the wireless network can be handled by rapaciously caching most frequently accessed contents by users. Hence, we propose an efficient popular content placement strategy, the first step in the content caching process, typically for popular video files. To do so, we introduce a novel approach for caching popular contents. This caching strategy follows a dynamic programming approach to tackle the optimization complexity of selecting most popular files among a wide range of files, under certain constraints. The proposed strategy gives the combination of popular files to be cached that maximizes the optimal cache hit probability with a pseudo-polynomial time complexity. To that end, we used the well-known resourcing algorithm, called the 0/1-Knapsack problem, assuming that files are cached without partitioning.

Today, billions of smart devices are interconnected via wireless networks, leading to large volumes of video contents circulating through the bandwidth-limited backhaul. This causes network performance to deteriorate. As a mitigation mechanism, caching of highly popular contents to network edges is deployed. We propose a cooperative and demandaware caching strategy, which is modelled using the Separable Assignment Problem, to maximize the cache hit ratio. This problem is solved with a recursive enumeration method, where dynamic programming is used to fill each edge. The extensive application-level evaluations show that the proposed strategy outperforms existing caching policies.

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