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