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.

翻译：在云端合作网络的回航链路上传输视频通常会遇到安全风险,而大多数现有研究都忽略了这种风险。视频服务的特点可以灵活调整编码率,并提供可接受的编码品质,使安全要求更容易得到满足,但通过对边缘缓冲施加更多的限制而与视频编码紧密结合。在本文中,通过考虑视频编码和边缘缓冲之间的相互作用,我们研究了云端合作网络无线回航链路上安全视频传输由QoE驱动的跨层优化经验的质量。首先,我们开发了一个基于视频编码和边缘缓冲的安全传输模式。通过将这一模式作为安全制约,我们随后制定了一个QoE驱动的联合优化问题,但前提是有限的现有缓冲能力。为了解决优化问题,我们提出了两种算法:近于最佳的交互算法(EC-VE)和低计算复杂性的贪婪算法(Greedy EC-VE)。模拟结果表明,我们提议的EC-VE可以在安全限制范围内大大改进用户的QE,而拟议的Greedy EC-VE可以获取贸易QQ。