QoE-driven Secure Video Transmission in Cloud-edge Collaborative Networks

TL;DR

This paper explores a cross-layer optimization approach for secure video transmission in cloud-edge networks, balancing QoE and security by considering video encoding and caching interactions.

Contribution

It introduces a novel security model integrating video encoding and edge caching, along with two algorithms for QoE optimization under security constraints.

Findings

EC-VE significantly improves user QoE within security limits

Greedy EC-VE offers a good QoE-security tradeoff with low complexity

Simulation confirms effectiveness of the proposed methods

Abstract

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.