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We model the scheduling of emerging wireless data services as a Markov decision process. These services are characterized by real-time downlink data transfers such as those needed for personalized traffic, weather and business updates. Characterizing each differing service by its own time-criticality as well as reward and penalty values, we can numerically compute an optimal Markov decision scheduling rule for serving these requests. These computations suggest that we can formulate a simple near optimal rule for scheduling these services efficiently.