Resource Allocation with Media Cloud
The resource allocation in media cloud have been studied extensively. Alasaad et al.  propose an algorithm for resource reservation in media cloud based on the prediction of demand for streaming capacity. It can maximally exploit the discounted rates offered in the tariffs, while ensuring the sufficient resource to be reserved. Hong et al.  present a media task QoS based resource allocation algorithm in media cloud, by considering the service satisfaction of multitask. Magedanz et al.  evaluate the effects of multiple factors in a large-scale cloud environment, by defining the metric for assessing the performance of cloud brokering systems. Yin et al.  study the operations of cloud computing and wireless networks in mobile computing environments by considering not only the spectrum efficiency but also the pricing information in the cloud.
Sardis et al.  introduce a novel concept of cloud-based mobile media service delivery in which services run on the localized public clouds. Ren and Schaar  develop an online algorithm that cloud operator can dynamically adjust the resource provisioning according to the time-varying wireless channel conditions. Aggarwal et al.  introduce a generalized framework to compute the amount of resource to support media services with a generic cost function. Lu et al.  propose a service provisioning model to manage the resources in the hybrid cloud where the profit can be maximized. Xu et al.  present an incentive scheme for the relay selection to encourage selfish mobile nodes to participate in bundle delivery, where the relay resource can be allocated based on a game theoretical model. Although the above works have made a lot of efforts for the resource allocation, the characteristics of mobile users have not been considered enough. In addition, how to efficiently use cloud brokers to allocate cloud resource has not been mentioned either.