The thin-client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small-footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin-client computing. Firstly, a mathematical performance model is derived for wireless thin-client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user-specified quality of service (QoS). Secondly, a proxy-based adaptation framework is developed for wireless thin-client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule-based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet-based image coding to provide scalable QoS that can degrade gracefully. Our thin-client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright © 2008 John Wiley & Sons, Ltd.