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Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in content-centric next generation 5G wireless networks by leveraging localized content storage and delivery. Caching generally works in two phases namely (i) storage phase where parts of popular content is pre-fetched and stored in caches at the network edge during time of low network load and (ii) delivery phase where content is distributed to users at times of high network load by leveraging the locally stored content. Cache-aided networks therefore have the potential to leverage storage at the network edge to increase bandwidth efficiency. In this dissertation we ask the following question - What are the theoretical and practical guarantees offered by cache aided networks for reliable content distribution while minimizing transmission rates and increasing network efficiency? We furnish an answer to this question by identifying fundamental Shannon-type limits for cache aided systems. To this end, we first consider a cache-aided network where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models - (i) centralized content delivery where demands are serviced by the central server; and (ii) device-to-device-assisted distributed delivery where demands are satisfied by leveraging the collective content of user caches. For such cache aided networks, we develop a new technique for characterizing information theoretic lower bounds on the fundamental storage-rate trade-off. Furthermore, using the new lower bounds, we establish the optimal storage-rate trade-off to within a constant multiplicative gap and show that, for the case of multiple demands per user, treating each set of demands independently is order-optimal. To address the concerns of privacy in multicast content delivery over such cache-aided networks, we introduce the problem of caching with secure delivery. We propose schemes which achieve information theoretic security in cache-aided networks and show that the achievable rate is within a constant multiplicative factor of the information theoretic optimal secure rate. We then extend our theoretical analysis to the wireless domain by studying a cloud and cache-aided wireless network from a perspective of low-latency content distribution. To this end, we define a new performance metric namely normalized delivery time, or NDT, which captures the worst-case delivery latency. We propose achievable schemes with an aim to minimize the NDT and derive information theoretic lower bounds which show that the proposed schemes achieve optimality to within a constant multiplicative factor of 2 for all values of problem parameters. Finally, we consider the problem of caching and content distribution in a multi-small-cell heterogeneous network from a reinforcement learning perspective for the case when the popularity of content is unknown. We propose a novel topology-aware learning-aided collaborative caching algorithm and show that collaboration among multiple small cells for cache-aided content delivery outperforms local caching in most network topologies of practical interest. The results presented in this dissertation show definitively that cache-aided systems help in appreciable increase of network efficiency and are a viable solution for the ever evolving capacity demands in the wireless communications landscape. Ph. D.