Wireless resources are scarce, shared and time-varying making resource allocation mechanisms, e.g., scheduling, a key and challenging element of wireless system design. In designing good schedulers, we consider three types of performance metrics: system capacity, quality of service (QoS) seen by users, and the energy expenditures (battery lifetimes) incurred by mobile terminals. In this dissertation we investigate the impact of scheduling policies on these performance metrics, their interactions, and/or tradeoffs, and we specifically focus on flow-level performance under stochastic traffic loads. In the first part of the dissertation we evaluate interactions among flow-level performance metrics when integrating QoS and best effort flows in a wireless system using opportunistic scheduling. We introduce a simple flow-level model capturing the salient features of bandwidth sharing for an opportunistic scheduler which ensures a mean throughput to each QoS stream on every time slot. We show that the integration of QoS and best effort flows results in a loss of opportunism, which in turn results in a reduction of the stability region, degradation in system capacity, and increased file transfer delay. In the second part of the dissertation we study several ways in which mobile terminals can backoff on their uplink transmit power (thus slow down their transmissions) in order to extend battery lifetimes. This is particularly effective when a wireless system is underloaded, so the degradation in the users' perceived performance can be negligible. The challenge, however, is developing a mechanism that achieves a good tradeoff among transmit power, idling/circuit power, and the performance customers will see. We consider systems with flow-level dynamics supporting either real-time or best effort (e.g., file transfers) sessions. We show that significant energy savings can be achieved by leveraging dynamic spare capacity. We then extend our study to the case where mobile terminals have multiple transmit antennas. In the third part of the dissertation we develop a framework for user association in infrastructure-based wireless networks, specifically focused on adaptively balancing flow loads given spatially inhomogeneous traffic distributions. Our work encompasses several possible user association objective functions resulting in rate-optimal, throughput-optimal, delay-optimal, and load-equalizing policy, which we collectively denote [alpha]-optimal user association. We prove that the optimal load vector that minimizes this function is the fixed point of a certain mapping. Based on this mapping we propose an iterative distributed user association policy and prove that it converges to the globally optimal decision in steady state. In addition we address admission control policies for the case where the system cannot be stabilized. / text