Emulation of Point Cloud Streaming over 5G Network

Author:

Makiyah Estabraq1,Khamees Nassr

Affiliation:

1. University of Information Technology and Communications

Abstract

Abstract Realistic digital representations of 3D objects and environments are now achievable because to recent developments in computer graphics, enabling real-time user interactions. Creating effective compression techniques and technologies that may take into account varied application limits has become a crucial problem due to the rising need for various point clouds. Future wireless networks are expected to undergo a paradigm change as a result of the 3GPP's 5G Advanced development. In this paper, we propose a complete system for streaming 3D high density point cloud data using a web-based streaming server with HTTP/2 protocol enabled, and compare results in two scenarios over WIFI and over 5G standalone network. Results have shown great outperformance over conventional work by decreasing inter-frame latency for large point cloud streaming by 16.8% for the case of 4 million points using http/2 over wifi, and by 71.51% over emulated 5G network. Streamed packets were also captured showing an increased frame rate of the same sample by 20.7% and 353% for the cases of wifi and 5G networks, respectively.

Publisher

Research Square Platform LLC

Reference32 articles.

1. 3GPP, O. (2010). Technical specification group radio access network; evolved universal terrestrial radio access (E-UTRA); further advancements for E‐UTRA physical layer aspects. 3GPP, Tech. Report TR 36.814 V9. 0.0.

2. An overview of 5G advanced evolution in 3GPP release 18;Lin X;IEEE Communications Standards Magazine,2022

3. Liu, Z., Li, Q., Chen, X., Wu, C., Li, J., & Ji, Y. (2023). Point Cloud Video Streaming in 5G Systems and Beyond: Challenges and Solutions. Authorea Preprints.

4. d’Eon, E., Harrison, B., Myers, T., & Chou, P. A. (2017). 8i voxelized full bodies-a voxelized point cloud dataset. ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006, 7(8), 11.

5. Hosseini, M., & Timmerer, C. (2018, June). Dynamic adaptive point cloud streaming. In Proceedings of the 23rd Packet Video Workshop (pp. 25–30).

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