||IEEE 802.11-based wireless networks have become increasingly popular due to the recent availability of affordable devices providing multi-rate capabilities. In this thesis, we study rate adaptation and resource allocation methodologies for 802.11-based wireless networks. Rate adaptation is a key functionality of 802.11 devices allowing them to cope with time-varying channel conditions and to maximize the throughput of point-to-point wireless links. We propose a novel rate adaptation algorithm, 'Smart Sender' for 802.11 Wireless Local Area Networks (WLANs), which utilizes both statistics and signal strength measurements to select the transmission rate that maximizes the link throughput. We also implement and evaluate the algorithm in commercial WLAN devices, which proves that Smart Sender responsive, has low overheads, and is robust to collision errors in various wireless environments. We further study rate adaptation for Vehicular Ad hoc Networks (VANETs) with an 802.11 physical layer. Different from traditional methods employing periodic channel quality estimations, we propose to use a learning algorithm MTRA (Model Tree based Rate Adaptation) which trains a packet error rate model for different data rates. In real time, MTRA takes multiple inputs from the environment and outputs a rate decision satisfying specific packet error rate requirements. This approach can effectively utilize the available environmental information (distance, SNR, speed, and so on) for timely rate adaptation, hence avoiding the estimation delay experienced by the legacy methods. By locating the optimal rate for current channel conditions as soon as possible, MTRA offers superior throughput performance compared to other popular methods. While rate adaptation exploits the multi-rate capability of an individual user, it also results in the well-known phenomenon of 'performance anomaly' among multiple rate-different users sharing the WLAN. Therefore, efficient resource allocation among multiple users is critical to the system performance. For downlink resource allocation, we develop a weighted fair scheduling based on adaptive rate control (WFS-ARC) framework for throughput optimization while satisfying temporal fairness. For resource allocation with TCP flows in a WLAN, we design a dual queue management (DQM) scheme for TCP congestion avoidance and uplink/downlink fairness provisioning. DQM also exploits opportunistic scheduling for high link efficiency. For future work, we will study resource allocation methodologies for wireless networks such as IEEE 802.11n WLANs and IEEE 802.16 mesh networks. We believe the approaches discussed in this thesis could contribute to future research on these next generation wireless networks, by providing a versatile and flexible structure for various systems.