Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology
Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology
复制标题
基于 5G-VANET 通信技术多媒体流的分布式强化学习的智能流量整形
作者：
A. A. Ahmed;S. Malebary;Waleed Ali;Omar M. Barukab
车辆在一种被称为车载自组织网络(VANET)的高移动性MANET技术中充当移动节点，该技术用于城市和农村地区以及高速公路上。基于5G(5G-VANET)的VANET为车辆驾驶提供了可靠的通信、更短的端到端延迟、更高的数据传输速率、合理的成本和有保证的体验质量(QOE)等先进设施。然而，这些最新技术的关键挑战是设计一种实时多媒体交通整形，在由于切换而导致的信道容量和数据速率的不可预测的变化下保持平稳的连通性，以实现路边单元之间的快速车辆移动。提出了一种基于分布式强化学习(RMDRL)的智能实时多媒体流量整形方法，用于控制发送到5G-VANET的流量数量和速率。该机制选择了量化参数、图片组和帧速率等编码参数的准确决策，用于处理5G-VANET上多媒体流所需的流量整形。此外，使用五个视频片段综合研究了上述三个编码参数的影响，以获得5G通信上实时多媒体流传输的最佳话务率值。该算法在峰值信噪比(PSNR)和端到端帧时延方面优于基线流量整形算法。这项研究将为汽车制造提供新的舒适设施，以增强5G-VANET上的数据通信系统。
Vehicles serve as mobile nodes in a high-mobility MANET technique known as the vehicular ad hoc network (VANET), which is used in urban and rural areas as well as on highways. The VANET, based on 5G (5G-VANET), provides advanced facilities to the driving of vehicles such as reliable communication, less end-to-end latency, a higher data rate transmission, reasonable cost, and assured quality of experience (QoE) for delivered services. However, the crucial challenge with these recent technologies is to design a real-time multimedia traffic shaping that maintains smooth connectivity under the unpredictable change of channel capacity and data rate due to handover for rapid vehicle mobility among roadside units. This research proposes a smart real-time multimedia traffic shaping to control the amount and the rate of the traffic sent to the 5G-VANET based on distributed reinforcement learning (RMDRL). The proposed mechanism selects the accurate decisions of coding parameters such as quantization parameters, group of pictures, and frame rate that are used to manipulate the required traffic shaping of the multimedia stream on the 5G-VANET. Furthermore, the impact of the aforementioned three coding parameters has been comprehensively studied using five video clips to achieve the optimal traffic rate value for real-time multimedia streaming on 5G communication. The proposed algorithm outperforms the baseline traffic shaping in terms of peak-signal-to-noise-ratio (PSNR) and end-to-end frame delay. This research will open new comfortable facilities for vehicle manufacturing to enhance the data communication system on the 5G-VANET.