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We investigate efficient schemes for data communication from a server (base station and access point) to a mobile terminal over a wireless channel of randomly fluctuating quality. The terminal user generates requests for data items. If the buffer (cache) of the terminal contains the requested data, no access delay/latency is incurred. If not, the data is downloaded from the server, and until becoming available locally at the terminal, the user incurs a delay cost. Moreover, a transmission/power cost is incurred to transmit the data over the wireless link at a dynamically selected power level. To lower both the access delay and transmission costs, the system may prefetch data predictively and cache them on the terminal (especially during high-link-quality periods), anticipating future user requests. The goal is to jointly minimize the overall latency and power costs by dynamically choosing what data to (pre)fetch, what power level to use, and when to use it. We develop a modeling framework (based on dynamic programming and controlled Markov chains) that captures essential performance trade-offs. It allows for the computation of optimal decisions regarding what data to (pre)fetch and what power levels to use. To cope with emerging complexities, we then design efficient online heuristics whose simulation analysis demonstrates substantial performance gains over standard approaches.