Abstract
Internet video traffic has been rapidly increasing and is further expected to increase with the emerging 5G applications, such as higher definition videos, the IoT, and augmented/virtual reality applications. As end users consume video in massive amounts and in an increasing number of ways, the content distribution network (CDN) should be efficiently managed to improve the system efficiency. The streaming service can include multiple caching tiers, at the distributed servers and the edge routers, and efficient content management at these locations affects the quality of experience (QoE) of the end users. In this paper, we propose a model for video streaming systems, typically composed of a centralized origin server, several CDN sites, and edge-caches located closer to the end user. We comprehensively consider different systems design factors, including the limited caching space at the CDN sites, allocation of CDN for a video request, choice of different ports (or paths) from the CDN and the central storage, bandwidth allocation, the edge-cache capacity, and the caching policy. We focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel and efficient algorithm accounting for the multiple design flexibilities. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. The implementation of a virtualized cloud system managed by Openstack demonstrates that the proposed algorithms can significantly improve the SDTP metric compared with the baseline strategies.