Adaptive cloud resource allocation for large-scale crowdsourced multimedia live streaming services

For the global-scale multimedia live streaming services, both of the cost-efficiency at the service provider side and the Quality of Experience (QoE) satisfaction at the viewer side need to be achieved. This is a difficult challenge because the request patterns of global live-streaming services are highly dynamic. In this paper, we solve this issue by cloud-based adaptive resource allocation. We first present a cloud-based multi-tier architecture, called MaaS (Media as a Service), which consists of four types of modules. The main issue that we focus on is the deployment of properly dimensioned MaaS modules in proper geographical regions. We take the QoE of the Dynamic Adaptive Streaming over HTTP viewers into account for this decision. We propose a combination of deep-learning based demand prediction scheme and a dynamic-programming based heuristic to make a good tradeoff between viewers’ QoE and the cloud resource cost. Extensive evaluation shows that the proposed scheme clearly outperforms the existing schemes.