  In an increasingly visual world, people with blindness and low vision (pBLV)
face substantial challenges in navigating their surroundings and interpreting
visual information. From our previous work, VIS4ION is a smart wearable that
helps pBLV in their day-to-day challenges. It enables multiple artificial
intelligence (AI)-based microservices such as visual scene processing,
navigation, and vision-language inference. These microservices require powerful
computational resources and, in some cases, stringent inference times, hence
the need to offload computation to edge servers. This paper introduces a novel
video streaming platform that improves the capabilities of VIS4ION by providing
real-time support of the microservices at the network edge. When video is
offloaded wirelessly to the edge, the time-varying nature of the wireless
network requires the use of adaptation strategies for a seamless video service.
We demonstrate the performance of an adaptive real-time video streaming
platform through experimentation with an open-source 5G deployment based on
open air interface (OAI). The experiments demonstrate the ability to provide
the microservices robustly in time-varying network loads.
