Video transmission over the backhaul link in cloudedge collaborative networks usually suffers security risks. Only a few existing studies focus on ensuring secure backhaul link transmission. However, video content characteristics, which have significant effects on quality of experience (QoE), are ignored in the study. In this paper, we investigate the QoE-driven crosslayer optimization of secure video transmission over the backhaul link in cloud-edge collaborative networks. First, we establish the secure transmission model for backhaul link by considering video encoding and MEC-caching in a distributed cache scenario. Then, based on the established model, a joint optimization problem is formulated with the objective of improving user QoE and reducing transmission latency under the constraints of MEC capacity. To solve the optimization problem, we propose two algorithms: a near optimal iterative algorithm based on relaxation and branch and bound method (MC-VEB), and a greedy algorithm with low computational complexity (Greedy MC-VEB). Simulation results show that our proposed MC-VEB can greatly improve the user QoE and reduce transmission latency within security constraints, and the proposed Greedy MC-VEB can obtain the tradeoff between the user QoE and the computational complexity.

中文翻译：

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云边缘协作网络中QoE驱动的安全视频传输

通过Cloudedge协作网络中的回程链路进行视频传输通常会遇到安全风险。现有的研究很少集中在确保安全的回程链路传输上。但是，对体验质量（QoE）产生重大影响的视频内容特征在研究中被忽略。在本文中，我们研究了QoE驱动的跨层优化，以在云边缘协作网络中通过回程链路传输安全视频。首先，通过在分布式缓存方案中考虑视频编码和MEC缓存，建立用于回程链路的安全传输模型。然后，基于建立的模型，提出了一种联合优化问题，其目标是在MEC容量的约束下提高用户QoE并减少传输延迟。为了解决优化问题，我们提出了两种算法：基于松弛和分支定界方法的近乎最佳迭代算法（MC-VEB），以及计算复杂度较低的贪婪算法（Greedy MC-VEB）。仿真结果表明，我们提出的MC-VEB可以大大提高用户的QoE，并在安全约束内减少传输时延，而贪婪的MC-VEB可以在用户QoE和计算复杂度之间取得折衷。