Anableps: Priority-aware super-resolution Video Caching with low latency for QoE-centric multi-user MEC networks

Abstract

As video traffic continues to dominate network data transmission, high playback latency has emerged as a critical bottleneck limiting the quality of streaming media services. While existing edge-assisted super-resolution methods mitigate latency in single-user scenes, they struggle to balance computational resource allocation and latency-sensitive task demands in multi-user scenes with constrained mobile edge computing (MEC) nodes. To this end, we propose Anableps, a multi-user MEC framework that integrates dynamic model selection and deep reinforcement learning-based scheduling to achieve efficient resource allocation and minimize latency. Specifically, we design a dynamic multi-granularity super-resolution loading model using optical flow field features to adaptively allocate light, medium, or heavy super-resolution models, optimizing both resource utilization and visual quality. Additionally, we design a dynamic multi-task scheduling algorithm based on deep Q-Network that dynamically adjusts system load and task urgency through adaptive penalty factors, ensuring efficient resource prioritization. Experimental results demonstrate that Anableps improves QoE by 12.7% compared to state-of-the-art methods, while enhancing video smoothness and reducing re-buffering time by 23.4% and 18.7%, respectively, which further highlights its effectiveness in optimizing streaming performance in resource-constrained environments.