ProCAVE: A Self-Adaptive, Full-Lifecycle Edge Caching Framework for Video Streaming via Predictive Bandwidth Estimation and Preference-Aware Deep Reinforcement Learning

2026-08-04

AI Summary

This work addresses the insufficient coordination between edge caching and adaptive bitrate (ABR) streaming caused by wireless bandwidth fluctuations and diverse user preferences. To this end, the authors propose an end-to-end adaptive edge caching framework that jointly models predictive bandwidth estimation, PPO-driven ABR decisions, and DDPG-based continuous cache control, enabling proactive bitrate selection and user-aware cache optimization. A lightweight Transformer architecture is employed for throughput prediction, facilitating efficient coordination under high-dimensional global states. Experimental evaluation using MovieLens user preference data and real-world Ghent 4G bandwidth traces demonstrates that the proposed approach significantly improves byte hit rate, reduces backhaul load, and outperforms baseline methods such as FlyCache, achieving substantial gains in Quality of Experience (QoE) metrics.

Abstract

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.

Problem

• edge caching
• video streaming
• user preferences
• network dynamics
• adaptive bitrate

Innovation

• Predictive Bandwidth Estimation
• Preference-Aware DRL
• Proactive Caching
• Edge Video Streaming
• Self-Adaptive Framework