Models, performances and evolutions toward 6G enabled Time-Sensitive Networking for XR

This project addresses key barriers to the adoption of the metaverse and anticipates the evolution toward 6G networks[1][2]. The metaverse, powered - among others - by immersive Extended Reality (XR) services, requires networks to support ultra-low latency, high reliability, and precise synchronization among flows. While current 5G systems enable enhanced broadband (eMBB) and reduced latency (URLLC), they fall short of delivering fully deterministic end-to-end performances (delays, jitter and throughput)[1][3][4]. 

On the other hand, Time-Sensitive Networking (TSN), defined by IEEE 802.1 standards [5]2, provides mechanisms such as bounded latency, traffic shaping, and clock synchronization. Extending these mechanisms and guarantees to 5G/6G wireless environments remains an unresolved challenge[6][7]. This requires coordinated and synchronized routing and scheduling mechanisms on the different segments of the network (access, Edge, Core and Cloud), including the radio varying channels and the virtualized/SDN shared environment for core network functions[8][9][10][11][12]. 

This PhD research aims to bridge this gap by investigating how TSN mechanisms can be integrated into 5G/6G architectures, protocols and algorithms to meet the stringent requirements of XR and other industrial applications and services in the metaverse context. 

The thesis will address both theoretical and experimental challenges. Theoretical contributions will go through: 

• The development of analytical models for XR traffic flows (e.g., VR/AR sessions, holographic telepresence, industrial digital twins) in metaverse scenarios.
• Proposal of TSN scheduling and queuing strategies adapted to wireless variability (stochastic, with fading, interference and retransmissions) with a mathematical framework that bridges deterministic scheduling with probabilistic wireless behavior.
• Extension of synchronization to virtual network functions (VNF) and Cloud-Native Network Functions (CNFs). 

In addition, the PhD candidate will co-implement and validate (within the hosting and supervising teams) the proposed solutions on a 5G/6G testbed enhanced with TSN-capable switches and XR devices. Representative XR workloads will be used to measure QoS (latency, jitter, packet loss) and QoE (immersion, interactivity) under different network configurations to demonstrate the feasibility of metaverse and XR service delivery in realistic network conditions. 

We believe these contributions will shape next-generation deterministic networking standards, inform industrial deployment strategies (telecom, gaming, manufacturing), and reinforce IMT’s and University of L'Aquila leadership in 5G/6G, XR and TSN research.