5G networks appear on the horizon with distinguished Quality of Service (QoS) requirements such as aggregated data rate and latency. Managing such networks in either a distributed or centralized manner to best utilize the available scarce resources is still a big challenge. Better mechanisms are needed for resource allocation. In this dissertation, we discuss three distinct research problems related to this theme. The first part addresses enhancing the solvability of network optimization problems. For the class of problems studied, we show that a traditionally-formulated model is insufficient from a problem-solving perspective. When the size of the problem increases, even state-of-the-art optimizers cannot obtain an optimal solution because of memory constraints. We show that augmenting the model with suitable additional constraints and structure enables the optimizer to derive optimal solutions, or significantly reduce the optimality gap. The second problem is optimal channel bonding in wireless LANs under demand uncertainty. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demand using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least β and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The third problem is to allocate resources with receiver characteristic awareness in a multiple radio access technology environment. We propose a novel adjacent channel interference (ACI)-aware joint channel and power allocation framework that takes into account receiver imperfections arising due to (i) imperfect image frequency rejection and (ii) analog-to-digital converter aliasing. As the overall problem is in the form of Mixed-Integer-Linear-Programming (MILP) which is NP-hard, we develop an efficient algorithm to solve it. / Ph. D. / The applications of next generation wireless networks have distinct requirements such as high speed for video streaming, low delay for interactive applications, and scalability to manage huge numbers of wireless devices. Managing such networks is challenging given the scarcity of wireless resources. In this dissertation, we discuss three distinct research problems related to this theme. The first part addresses enhancing the solvability of network optimization problems. State-of-the-art commercial optimization tools are unable to solve these problems for reasonable network sizes. We propose multiple strategies that help the tool obtain optimal solutions quickly. The second part considers indoor wireless networks. For such a network, we propose a technique that matches the instantaneous resources allocated to each location in the network with the amount of data traffic currently at the location. The third part addresses a problem of a network with multiple wireless transmitters and receivers where each receiver suffers from interference from other transmitters differently. We develop an algorithm to allocate resources and adjust transmit power so that each pair can communicate while meeting a minimum required data rate. The three parts of the dissertation are useful in either saving resources and hence allowing more users to use the network, or providing higher service quality for wireless device users.