Cloud-edge collaboration for cost-effective video service provisioning

The advances of personal computing devices and the prevalence of high-speed Internet access have pushed video streaming services into a new era. One of its representative examples is crowdsourced livecast services where numerous amateur broadcasters lively stream their video contents to viewers around the world. For video service providers, processing these multimedia contents is inherently resource-intensive, time-consuming, and consequently expensive. The demand for low latency to guarantee interactivity in these emerging services further challenges the prevalent cloud-based solutions. In this thesis, we start by revealing the potentials of offering cost-effective low-latency video services both at the cloud and the edge side through analyzing the traces collected from real-world applications. We then examine the feasibility of an instance subletting service at the cloud side, where idle cloud resources can be traded. The performance of such a service is examined from both theoretical and practical perspectives. To satisfy the low-latency requirement in the emerging interaction-rich video services, we propose a crowd transcoding solution, which fully relies on powerful users to finish transcoding. To further improve the stability of such a distributed computing system, we then propose a cloud-crowd collaborative solution, which combines redundant end viewers with the cloud to perform video processing tasks cost-effectively. Novel probabilistic auction mechanisms are designed to facilitate this solution with desirable economic properties guaranteed.