Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology

Abstract

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.

A Deep Reinforcement Learning-Based RNN Model in a Traffic Control System for 5G-Envisioned Internet of Vehicles

“…In order to regulate the volume and velocity of data transmitted via the 5G-VANET, Ahmed et al [19] propose a smart real-time shaping system that makes use of distributed reinforcement learning (RMDRL). In order to control the necessary traffic multimedia stream over the 5G-VANET, the suggested system picks the accurate judgements of coding parameters and rates.…”

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

“…The work in [27] addressed issues of location privacy and reliability in VANET routing protocols, proposing DARVAN, a fully decentralized, anonymous, and reliable routing for VANETs that maximizes privacy and reliability using distributed databases and collective consensus. Furthermore, authors in [28] proposed an Intelligent Real-time Multimedia Traffic Shaping based on Reinforcement Multi-Distributed Learning (RMDRL) to control the flow and rate of traffic sent to 5G-VANETs. While VANETs can transmit media data and perform well in some scenarios, they are not suitable for media data transmission in high-speed train scenarios or high-speed networks.…”

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