ProCAVE通过预测带宽和偏好学习，实现视频流的主动边缘缓存。
ProCAVE: A Self-Adaptive, Full-Lifecycle Edge Caching Framework for Video Streaming via Predictive Bandwidth Estimation and Preference-Aware Deep Reinforcement Learning
- 用轻量Transformer预测带宽，结合强化学习动态选码率和缓存策略。
- 相比FlyCache，缓存命中率提升18.7%，回程负载降低23.4%。
- 适合追求低延迟、高体验的移动视频边缘部署场景。
移动视频流需求增长对边缘分发系统提出了快速响应网络波动与用户偏好的要求。现有方法如FlyCache依赖被动ABR启发式规则和松散耦合的缓存策略，在真实无线环境下响应迟滞、协同不足。本文提出ProCAVE（主动缓存自适应视频体验），一个基于深度强化学习的全生命周期自适应框架，统一了预测性带宽建模、主动码率选择与偏好感知缓存控制。ProCAVE采用：(i) 轻量级Transformer进行短期吞吐量预测；(ii) PPO驱动的ABR智能体；(iii) 基于DDPG的连续缓存控制器，在高维全局状态空间上运行。基于MovieLens用户偏好数据与Ghent 4G实测带宽的实验表明，与FlyCache及其他基线相比，ProCAVE显著提升了字节命中率、降低了回程负载，并优化了用户体验质量（QoE）。
原文摘要 · Abstract (English)
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.
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