In this paper, we present a media adaptation framework for an immersive biofeedback system for stroke patient rehabilitation. In our biofeedback system, media adaptation refers to changes in audio/visual feedback as well as changes in physical environment. Effective media adaptation frameworks help patients recover generative plans for arm movement with potential for significantly shortened therapeutic time. The media adaptation problem has significant challenges - (a) high dimensionality of adaptation parameter space (b) variability in the patient performance across and within sessions(c) the actual rehabilitation plan is typically a non first-order Markov process, making the learning task hard. Our key insight is to understand media adaptation as a real-time feedback control problem. We use a mixture-of-experts based Dynamic Decision Network (DDN) for online media adaptation. We train DDN mixtures per patient, per session. The mixture models address two basic questions - (a) given a specific adaptation suggested by the domain expert, predict patient performance and (b) given an expected performance, determine optimal adaptation decision. The questions are answered through an optimality criterion based search on DDN models trained in previous sessions. We have also developed new validation metrics and have very good results for both questions on actual stroke rehabilitation data.