云边缘协作网络中的QoE-driven安全视频传输
QoE-driven Secure Video Transmission in Cloud-edge Collaborative Networks
摘要：
在云边缘协作网络中，回传链路上的视频传输通常存在安全风险，这一问题在现有的研究中被忽略。视频业务可以灵活调整编码速率，提供可接受的编码质量，这一特点使得安全需求更有可能得到满足，但通过对边缘缓存引入更多的限制，使得安全需求与视频编码紧密耦合。在本文中，通过考虑视频编码和边缘缓存之间的相互作用，我们研究了体验质量（QoE）驱动的跨层优化的安全视频传输在云边缘协作网络的无线回程链路。首先，我们开发了一个基于视频编码和边缘缓存的安全传输模型。通过采用这个模型作为安全约束，然后我们制定了一个QoE驱动的联合优化问题受到有限的可用缓存容量。为了解决这一优化问题，我们提出了两种算法：一种近似最优迭代算法（EC-VE）和一种低计算复杂性的贪心算法（Greedy EC-VE）。仿真结果表明，该算法在满足安全约束的前提下，能够显著提高用户的QoE，并且在QoE和计算复杂性之间取得了较好的折衷。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
