An Open Framework for Analyzing and Modeling XR Network Traffic
This work addresses the need for better traffic models in XR applications to support optimized network algorithms, though it is incremental as it builds on existing data and proposes a framework rather than a complete solution.
The paper tackles the lack of models for simulating and analyzing XR network traffic by presenting a novel open-source traffic model and sharing collected traces from applications like Minecraft VR, Google Earth VR, and Virus Popper, with the goal of enabling improved performance and Quality of Experience for users.
Thanks to recent advancements in the technology, eXtended Reality (XR) applications are gaining a lot of momentum, and they will surely become increasingly popular in the next decade. These new applications, however, require a step forward also in terms of models to simulate and analyze this type of traffic sources in modern communication networks, in order to guarantee to the users state of the art performance and Quality of Experience (QoE). Recognizing this need, in this work, we present a novel open-source traffic model, which researchers can use as a starting point both for improvements of the model itself and for the design of optimized algorithms for the transmission of these peculiar data flows. Along with the mathematical model and the code, we also share with the community the traces that we gathered for our study, collected from freely available applications such as Minecraft VR, Google Earth VR, and Virus Popper. Finally, we propose a roadmap for the construction of an end-to-end framework that fills this gap in the current state of the art.