EASR: Enabling Neural-Enhanced Video Streaming on Mobile Devices with Edge Assistance

Neural-enhanced video streaming systems have successfully addressed the challenge of limited network bandwidth by utilizing super-resolution (SR) techniques to enhance video quality. However, mobile users often face constraints in terms of computational resources required for SR operations. To overcome this, the integration of mobile edge computing becomes crucial. The main challenge lies in efficiently allocating GPU resources on the edge server to multiple users as the available GPU resources are typically insufficient to process all video chunks. In this paper, we formulate the problem of an edge server assisting multiple users in selecting bitrate levels and performing SR inference, and prove its NP-hardness. Subsequently, we propose an edge-assisted video streaming framework named Edge-Assisted SR (EASR) for high-definition neural-enhanced video streaming. This framework is built upon the theory of model predictive control (MPC). EASR addresses the variable reward of SR enhancement across different video chunks by making joint decisions on bitrate, SR, and GPU allocation for each chunk to maximize the average quality of experience (QoE) for all users. We evaluate the performance of EASR on four videos and real network traces. Extensive experiments reveal that EASR outperforms other baselines by $18.76 \%$ to $58.37 \%$ in terms of average QoE and 0.005 to 0.017 in terms of structural similarity index (SSIM).