2022/01/18 by Mattia Lecci, Federico Chiariotti, Lecci, Mattia +8 · 2 citations
Computer Science · Engineering · Social Sciences · #Advanced Computing and Algorithms #Caching and Content Delivery #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image and Video Quality Assessment #Multimedia (cs.MM) #Networking and Internet Architecture (cs.NI) #cs.MM #cs.NI #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.07043
10 pages, 10 figures. The paper has been submitted to IEEE WoWMoM 2022. Copyright may change without notice
arxiv created 2022/01/18 · openalex publication_date 2022/01/18 · arxiv updated 2022/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.