Cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio system. In CRANs, joint signal processing is performed at multiple cloud computing centers (clouds) that are connected to several base stations (BSs) via high capacity backhaul links. As a result, large-scale interference management and network power consumption reduction can be effectively achieved. Unlike recent works on CRANs which consider a single cloud processing and treat inter-cloud interference as background noise, the first part of this thesis focuses on the more practical scenario of the downlink of a multi-cloud radio access network where BSs are connected to each cloud through wireline backhaul links. Assume that each cloud serves a set of pre-known single-antenna mobile users (MUs). This part focuses on minimizing the network total power consumption subject to practical constraints. The problems are solved using sophisticated techniques from optimization theory (e.g. Dual Decomposition-based algorithm and the alternating direction method of multipliers (ADMM)-based algorithm). One highlight of this part is that the proposed solutions can be implemented in a distributed fashion by allowing a reasonable information exchange between the coupled clouds. Additionally, feasible solutions of the considered optimization problems can be estimated locally at each iteration. Simulation results show that the proposed distributed algorithms converge to the centralized algorithms in a reasonable number of iterations. To further account of the backhaul congestion due to densification in CRANs, the second part of this thesis considers the downlink of a cache-enabled CRAN where each BS is equipped with a local-cache with limited size used to store the popular files without the need for backhauling. Further, each cache-enabled BS is connected to the cloud via limited capacity backhaul link and can serve a set of pre-known single antenna MUs. This part assumes that only imperfect channel state information (CSI) is available at the cloud. This part focuses on jointly minimizing the network total power consumption as well as backhaul cost. It then suggests solving this optimization problem using the majorization-minimization (MM) approach. Simulation results show that the proposed algorithm converges in a reasonable number of iterations.