Qoe-Driven Has Live Video Channel Placement In The Media Cloud

HTTP adaptive streaming (HAS) technology has been increasingly employed by video service providers (VSPs) due to its prominent benefits such as reducing interruptions of video playback and achieving higher bandwidth utilization and outstanding quality of experience (QoE). And many VSPs have deployed HAS applications in the media cloud to provide large-scale video streaming services. At present, research into the media cloud typically focuses on the management and optimization of cloud resources, such as the placement and migration of virtual machines in media cloud data centers. However, considering the HAS live video streaming service, existing related works have not adequately discussed the specific impact of the consumption of computing and bandwidth resources of media cloud servers on the user experience (QoE), particularly under the resource constraints in the media cloud. In this paper, we first investigate and formulate the computing and bandwidth resource consumption characteristics of HAS live video streaming with different frame rates and resolutions, and we further establish a resources-aware QoE model to quantify the user experience of live video channels (i.e., programs). Then, based on the model, we p rest nt a QoE-driven HAS live video channel placement approach (including a placement algorithm HCP and a rescheduling algorithm HCR) to optimize the channel allocation in media cloud servers, aiming to maximize the average user QoE. We abstract the maximization problem into an MMKP problem, and employ a heuristic solution to address this problem. The experimental results demonstrate the effectiveness of our proposed approach compared with benchmark solutions.