Enabling efficient and high quality zooming for online video streaming using edge computing

High quality zooming function for online video streaming using cloud content servers remains a challenge due to the intertwined relationships among video chunk lengths, viewer's fast changing Region of Interest (RoI), and network latency. It is possible to utilize tiled Video technique and store picture tiles in separate files with their unique URLs on the media server with smaller chunk sizes, however it introduces a significant burden on the network core due to increased total video length contributed by combined non-video bits from too many smaller chunks. To overcome this, in this paper we propose the use of edge computing to achieve high quality zooming function for video steaming. Our proposal includes the system architecture using Tiled-DASH (T-DASH) video encoding on edge servers, and a novel ROI prediction method combining three different prediction models: online, offline and object-level prediction models on the client side. Our evaluations show that a high level of ROI prediction accuracy is achieved by our approach, fulfilling a core condition for making the zooming function a reality.