  Internet-enabled smartphones and ultra-wide displays are transforming a
variety of visual apps spanning from on-demand movies and 360{\deg} 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 components and architecture of existing content delivery
systems, highlighting their challenges and motivating the use of neural
enhancement models as a countermeasure. We then cover the deployment challenges
of these models and analyze existing systems and their design decisions in
efficiently overcoming these technical challenges. Additionally, we underline
the key trends and common approaches across systems that target diverse
use-cases. Finally, we present promising future directions based on the latest
insights from deep learning research to further boost the quality of experience
of content delivery systems.
