AI 中文总结
该研究针对现有边缘缓存方法在无线动态环境下响应与协调不足的问题，提出ProCAVE框架，结合轻量Transformer、PPO、DDPG实现预测带宽建模与偏好感知缓存控制，实验表明其在字节命中率、回传负载、QoE上优于FlyCache等基线。
AI 中文摘要
移动视频流日益增长的需求要求边缘交付系统能高效适配快速网络波动与多样用户偏好。现有方法如FlyCache依赖反应式ABR启发式算法与松散耦合的缓存策略，在真实无线动态环境下响应性与协调性受限。我们提出ProCAVE（Proactive Caching with Adaptive Video Experience，即带自适应视频体验的主动缓存），一种基于深度强化学习（DRL）的自适应框架，统一了预测带宽建模、主动码率选择与偏好感知缓存控制。ProCAVE采用：（i）用于短期吞吐量预测的轻量Transformer；（ii）基于PPO的ABR智能体；（iii）基于DDPG的连续缓存控制器，在高维全局状态下运行。使用MovieLens偏好轨迹与根特4G带宽测量的实验显示，与FlyCache及其他基线相比，ProCAVE提升了字节命中率，降低了回传负载并增强了QoE。这些结果凸显了基于预测、DRL协调的控制在高效、以用户为中心的边缘视频交付中的优势。
英文摘要
The growing demand for mobile video streaming requires edge delivery systems that adapt efficiently to rapid network fluctuations and diverse user preferences. Existing approaches such as FlyCache rely on reactive ABR heuristics and loosely coupled cache policies, limiting their responsiveness and coordination under real-world wireless dynamics. We propose ProCAVE (Proactive Caching with Adaptive Video Experience), a self-adaptive DRL-based framework that unifies predictive bandwidth modeling, proactive bitrate selection, and preference-aware cache control. ProCAVE employs: (i) a lightweight Transformer for short-term throughput forecasting; (ii) a PPO-driven ABR agent; and (iii) a DDPG-based continuous cache controller operating on a high-dimensional global state. Experiments using MovieLens preference traces and Ghent 4G bandwidth measurements show that ProCAVE improves byte hit rate, reduces backhaul load, and enhances QoE compared with FlyCache and other baselines. These results highlight the benefits of predictive, DRL-coordinated control for efficient and user-centric edge video delivery.
Comments5 pages
