QoE-driven Secure Video Transmission in Cloud-edge Collaborative Networks
This work tackles the problem of secure video transmission for users in cloud-edge collaborative networks, providing an incremental improvement by integrating security into QoE optimization.
This paper addresses the security risks in video transmission over backhaul links in cloud-edge collaborative networks, which is often overlooked. The authors propose a QoE-driven cross-layer optimization framework that considers the interplay between video encoding and edge caching to enhance security while improving user QoE.
Video transmission over the backhaul link in cloud-edge collaborative networks usually suffers security risks, which is ignored in most of the existing studies. The characteristics that video service can flexibly adjust the encoding rates and provide acceptable encoding qualities, make the security requirements more possible to be satisfied but tightly coupled with video encoding by introducing more restrictions on edge caching. In this paper, by considering the interaction between video encoding and edge caching, we investigate the quality of experience (QoE)-driven cross-layer optimization of secure video transmission over the wireless backhaul link in cloud-edge collaborative networks. First, we develop a secure transmission model based on video encoding and edge caching. By employing this model as the security constraint, then we formulate a QoE-driven joint optimization problem subject to limited available caching capacity. To solve the optimization problem, we propose two algorithms: a near-optimal iterative algorithm (EC-VE) and a greedy algorithm with low computational complexity (Greedy EC-VE). Simulation results show that our proposed EC-VE can greatly improve user QoE within security constraints, and the proposed Greedy EC-VE can obtain the tradeoff between QoE and computational complexity.