Video traffic constitutes a major part of the Internet traffic nowadays. Yet most video delivery services remain best-effort, relying on server bandwidth over-provisioning to guarantee Quality of Service (QoS). Cloud computing is changing the way that video services are offered, enabling elastic and efficient resource allocation through auto-scaling. In this thesis, we propose a new framework of cloud workload management for multimedia delivery services, incorporating demand forecast, predictive resource allocation and quality assurance, as well as resource pricing as inter-dependent components. Based on the trace analysis of a production Video-on-Demand (VoD) system, we propose time-series techniques to predict video bandwidth demand from online monitoring, and determine bandwidth reservations from multiple data centers and the related load direction policy. We further study how such quality-guaranteed cloud services should be priced, in both a game theoretical model and an optimization model.Particularly, when multiple video providers coexist to use cloud resources, we use pricing to control resource allocation in order to maximize the aggregate network utility, which is a standard network utility maximization (NUM) problem with coupled objectives. We propose a novel class of iterative distributed solutions to such problems with a simple economic interpretation of pricing. The method proves to be more efficient than the conventional approach of dual decomposition and gradient methods for large-scale systems, both in theory and in trace-driven simulations.