Adaptive beamforming is a. powerful approach to receive or transmit signals of interest in a spatially selective way in the presence of interference and noise. Recently, there has been renewed interest in adaptive beamforming driven by applications in wireless communications, where multiple-input multiple-output (MEMO) techniques have emerged as one of the key technologies to accommodate the high number of users as well as the increasing demand for new high data rate services. Beamforming techniques promise to increase the spectral efficiency of next generation wireless systems and are currently being incorporated in future industry standards. Although a significant amount of research has focused on theoretical capacity analysis, little is known about the performance of such systems in practice. In thesis, I experimentally and analytically evaluate the performance of adaptive beamforming techniques on the downlink channel of a wireless LAN. To this end. I present the design and implementation of the first multi-user beam-forming system and experimental framework for wireless LANs. Next, I evaluate the benefits of such system in two applications. First, I investigate the potential of beamforming to increase the unicast throughput through spatial multiplexing. Using extensive measurements in an indoor environment, I evaluate the impact of user separation distance, user selection, and user population size on the multiplexing gains of multi-user beamforming. I also evaluate the impact of outdated channel information due to mobility and environmental variation on the multiplexing gains of multi-user beamforming. Further, I investigate the potential of beamforming to eliminate interference at unwanted locations and thus increase spatial reuse. Second, I investigate the potential of adaptive beamforming for efficient wireless multicasting. I address the joint problem of adaptive beamformer design at the PHY layer and client scheduling at the MAC layer by proposing efficient algorithms that are amenable to practical implementation. Next, I present the implementation of the beamforming based multicast system on the WARP platform and compare its performance against that of omni-directional and switched beamforming based multicast. Finally, I evaluate the performance of multicast beamforming under client mobility and infrequent channel feedback, and propose solutions that increase its robustness to channel dynamics.