Providing Quality of Service (QoS) to emerging time-sensitive applications such as factory automation, telesurgery, and VR/AR applications is a challenging task [1]. Time Sensitive Networks (TSN) [2] and Deterministic Networks [3] were developed for such applications to guarantee ultra low latency, bounded latency and jitter, and zero congestion loss. The objective of this work is to develop a methodology to guarantee bounded End-to-End (E2E) latency and jitter in large-scale networks.

Prerequisites

Modern mobile robots often use cameras to perceive their environment or to allow remote control by a human operator. These video streams require a reliable network connection, especially when transmitted over 5G. However, network conditions in 5G can change dynamically due to movement, signal quality, or network load. This can lead to delays, reduced video quality, or even connection issues. This project focuses on making the robot “smart” in how it sends video data: instead of using fixed settings, the video stream should automatically adapt to current network conditions.

The aim is to develop an application running on a robot’s onboard computer that adjusts the quality of a stereo camera video stream based on real-time 5G network conditions. The system should balance video quality and reliability, i.e., video stability and availability, to achieve smooth and efficient transmission.