  Improving efficiency of healthcare systems is a top national interest
worldwide. However, the need of delivering scalable healthcare services to the
patients while reducing costs is a challenging issue. Among the most promising
approaches for enabling smart healthcare (s-health) are edge-computing
capabilities and next-generation wireless networking technologies that can
provide real-time and cost-effective patient remote monitoring. In this paper,
we present our vision of exploiting multi-access edge computing (MEC) for
s-health applications. We envision a MEC-based architecture and discuss the
benefits that it can bring to realize in-network and context-aware processing
so that the s-health requirements are met. We then present two main
functionalities that can be implemented leveraging such an architecture to
provide efficient data delivery, namely, multimodal data compression and
edge-based feature extraction for event detection. The former allows efficient
and low distortion compression, while the latter ensures high-reliability and
fast response in case of emergency applications. Finally, we discuss the main
challenges and opportunities that edge computing could provide and possible
directions for future research.
