This research is aimed at providing
theoretically rigorous, flexible, efficient
methodologies for intelligent delivery of data in a dynamic and resource constrained environment. Our proposed solution utilizes a uniform client and server profilization for data delivery and describe the challenges in developing optimized hybrid data delivery schedules. We also present an approach that aims at constructing automatic adaptive policies for data delivery to overcome various modeling errors utilizing feedback.