The Enhanced Pay-Per-View (EPPV) model for providing continuous-media services associates with each continuous-media clip a display frequency that depends on the clip's popularity. The aim is to increase the number of clients that can be serviced concurrently beyond the capacity limitations of available resources, while guaranteeing a constraint on the response time. This is achieved by sharing periodic continuous-media streams among multiple clients. The EPPV model offers a number of advantages over other data-sharing schemes (e.g., batching), which make it more attractive to large-scale service providers. In this paper, we provide a comprehensive study of the resource-scheduling problems associated with supporting EPPV for continuous-media clips with (possibly) different display rates, frequencies, and lengths. Our main objective is to maximize the amount of disk bandwidth that is effectively scheduled under the given data layout and storage constraints. Our formulation gives rise to -hard combinatorial optimization problems that fall within the realm of hard real-time scheduling theory. Given the intractability of the problems, we propose novel heuristic solutions with polynomial-time complexity. We also present preliminary experimental results for the average case behavior of the proposed scheduling schemes and examine how they compare to each other under different workloads. A major contribution of our work is the introduction of a robust scheduling framework that, we believe, can provide solutions for a variety of realistic EPPV resource-scheduling scenarios, as well as any scheduling problem involving regular, periodic use of a shared resource. Based on this framework, we propose various interesting research directions for extending the results presented in this paper.