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Over the past few years, eXtended Reality (XR) has attracted increasinginterest thanks to its extensive industrial and commercial applications, andits popularity is expected to rise exponentially over the next decade. However,the stringent Quality of Service (QoS) constraints imposed by XR's interactivenature require Network Slicing (NS) solutions to support its use over wirelessconnections: in this context, quasi-Constant Bit Rate (CBR) encoding is apromising solution, as it can increase the predictability of the stream, makingthe network resource allocation easier. However, traffic characterization of XRstreams is still a largely unexplored subject, particularly with this encoding.In this work, we characterize XR streams from more than 4 hours of tracescaptured in a real setup, analyzing their temporal correlation and proposingtwo prediction models for future frame size. Our results show that even thestate-of-the-art H.264 CBR mode can have significant frame size fluctuations,which can impact the NS optimization. Our proposed prediction models can beapplied to different traces, and even to different contents, achieving verysimilar performance. We also show the trade-off between network resourceefficiency and XR QoS in a simple NS use case.

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing