Optimal Content Management and Dimensioning in Wireless Networks

The massive increase in cellular traffic poses serious challenges to all actors concerned with wireless content delivery. While network densification provides access to additional users, high-speed and high-capacity backhaul connections are expensive. Caching popular content at the network edge promises to offload user traffic from these congestion prone connections as well as from the data centers in the backbone network. This thesis proposes a business model in which a mobile network operator (MNO) pre-installs and maintains caches at its wireless equipment (Cache-equipped Base Stations, CBSs). Memory space together with computational capabilities is then leased to content providers (CPs) that want to bring their content closer to the user. For a financial compensation, a CP can then offload traffic from its data center and improve user Quality of Service. The CP makes content placement decisions based on predictive user traffic and content popularity data. In the delivery phase, users can be served from the caches in case they are associated to stations that have the requested content cached. This work investigates three aspects of the proposed business model: The first research question focuses on user association as a central element to the edge caching scheme. Cache-aware user association policies can allow for users in coverage overlap areas to be associated to a CBS that holds the requested content rather than conventionally to the one that provides the strongest signal. The thesis proposes an original decentralized algorithm for user association called Generalized Bucket-filling that allows gains beyond maximizing the hit ratio. Performance metrics such as network throughput and load balancing of users among CBSs are taken into account. Experiments show that cache-aware user association a) increases the hit ratio b) without overloading single CBSs while c) providing high system throughput. The second problem treated considers a single CP that needs to decide how much cache space to lease at each CBS for a fixed price, and what content to place. Its choices should be based on estimates of file popularity as well as MNO user association policy. The cache leasing and content placement problem is formulated as a non-linear mixed-integer problem (NLMIP). In its solution, the problem is separated into a linear discrete CP subproblem and a nonlinear continuous subproblem using Benders decomposition. The CP and the MNO cooperate, helping the CP to make optimal decisions that benefit both parties: The CP maximizes its savings from caching while the MNO can find the optimal cache price and receive the maximum financial compensation. A third research question widens the focus to the interaction between several CPs and one MNO. Now, the MNO does not set a fixed price per memory unit but instead reacts to CP demands for memory space that depend on the savings they can achieve from caching.