Video Communication Optimization Using Distributed Edge Computing

We proposes a backbone network resource optimization algorithm for video communications that use edge computing. In the current video communication architecture, the key component of video communication, called the multi-point control unit (MCU), is deployed in the central cloud server, and its bandwidth consumption in the backbone network becomes enormous as the video resolution and the frequency of use increase. By implementing edge computing, the MCU can be deployed at the entrance node of the backbone network. This allows (i) a local loopback of video traffic at an edge, and (ii) traffic compression (e.g., thumbnailing) between edge nodes. Though these characteristics can reduce the resource consumption of the backbone network, the edge deployment and routing (EDR) problem, classified as NP-hard, should be solved to sufficiently reduce the bandwidth. To solve the NP-hard EDR problem within a feasible period, we propose a divide and merge algorithm based on the linear programming approach. With our algorithm, bandwidth consumption using edge computing is reduced by approximately 30% compared with the current video communication architecture in the world-wide network.