Abstract
This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-based control center. The range of topics includes all stages of video and LiDAR data processing from acquisition and encoding to final decoding, all aspects of their transmission and reception via the WebRTC protocol, and all possible types of network problems such as handovers or congestion that could affect the quality of experience for end-users. A series of experiments were conducted to evaluate the key aspects of the data transmission. These include simulation-based reproducible runs and real-world experiments conducted using open-source solutions we developed: "Gymir5G" - an OMNeT++-based 5G simulation and "GstWebRTCApp" - a GStreamer-based application for adaptive control of media streams over the WebRTC protocol. One of the goals of this study is to formulate the bandwidth and latency requirements for reliable real-time communication and to estimate their approximate values. This goal was achieved through simulation-based experiments involving docking maneuvers in the Bay of Kiel, Germany. The final latency for the entire data processing pipeline was also estimated during the real tests. In addition, a series of simulation-based experiments showed the impact of key WebRTC features and demonstrated the effectiveness of the WebRTC protocol, while the conducted video codec comparison showed that the hardware-accelerated H.264 codec is the best. Finally, the research addresses the topic of adaptive communication, where the traditional congestion avoidance and deep reinforcement learning approaches were analyzed. The comparison in a sandbox scenario shows that the AI-based solution outperforms the WebRTC baseline GCC algorithm in terms of data rates, latency, and packet loss.
Abstract (translated)
这一概念分析研究了5G网络数据传输的动态。它涉及从遥控渡轮上的摄像头和激光雷达发送数据到陆地控制中心的各种方面。主题范围包括从采集和编码到最终解码的视频和激光雷达数据处理的所有阶段，以及通过WebRTC协议传输和接收数据的所有方面，以及可能影响用户体验的各种网络问题，如切换或拥塞。进行了一系列实验来评估数据传输的关键方面。这些包括基于模拟的重复实验和基于我们开发的Open Source解决方案进行的真实世界实验：“Gymir5G” - 一个基于OMNeT++的5G模拟和“GstWebRTCApp” - 一个基于GStreamer的WebRTC应用程序，用于在WebRTC协议上 adaptive控制媒体流。本研究的一个目标是制定可靠实时通信的带宽和延迟要求，并估计其近似值。通过基于模拟的实验在德国基尔湾的港口进行系泊操纵来达到这个目标。在真实测试期间，还估计了整个数据处理流程的最终延迟。此外，一系列基于模拟的实验展示了关键WebRTC功能对性能的影响，并证明了WebRTC协议的有效性，而进行的视频编解码比较则表明了硬件加速的H.264编解码器是最好的。最后，研究关注自适应通信，其中传统的避免拥塞和深度强化学习方法进行了分析。在沙盒场景中的比较表明，基于AI的解决方案在数据率、延迟和包丢失方面优于基于WebRTC基线的GCC算法。
URL
https://arxiv.org/abs/2404.16508
