  Internet-enabled smartphones and ultra-wide displays are transforming a
variety of visual apps spanning from on-demand movies and 360-degree videos to
video-conferencing and live streaming. However, robustly delivering visual
content under fluctuating networking conditions on devices of diverse
capabilities remains an open problem. In recent years, advances in the field of
deep learning on tasks such as super-resolution and image enhancement have led
to unprecedented performance in generating high-quality images from low-quality
ones, a process we refer to as neural enhancement. In this paper, we survey
state-of-the-art content delivery systems that employ neural enhancement as a
key component in achieving both fast response time and high visual quality. We
first present the deployment challenges of neural enhancement models. We then
cover systems targeting diverse use-cases and analyze their design decisions in
overcoming technical challenges. Moreover, we present promising directions
based on the latest insights from deep learning research to further boost the
quality of experience of these systems.
