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In next generation data networks, joint optimization of physical layer parameters and scheduling layer parameters will be necessary to meet the demands of quality of service (QoS)-constrained traffic like video traffic. Until now, such optimization problems considered only simple and non-realistic Markov chain models to represent physical layer channel dynamics. In this paper, we consider the incorporation of realistic channel models in a Markov decision process (MDP) formulation for the QoS-constrained optimization of the physical layer. The channel random process is transformed into an extended Markovian setup through hidden Markov models (HMM) and the solution of the resulting optimization problem is shown to be a partially observable Markov decision process (POMDP). The optimal scheduling agent bases its decision on a parameter that summarizes complete system history until the decision instant. This parameter can he computed at each decision instant based only on newly available information, avoiding the need to record all the past states.