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

Author

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  • Adel A. Ahmed

    (Information Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi Arabia)

  • Sharaf J. Malebary

    (Information Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi Arabia)

  • Waleed Ali

    (Information Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi Arabia)

  • Omar M. Barukab

    (Information Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi Arabia)

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.

Suggested Citation

  • Adel A. Ahmed & Sharaf J. Malebary & Waleed Ali & Omar M. Barukab, 2023. "Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology," Mathematics, MDPI, vol. 11(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:700-:d:1051296
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    References listed on IDEAS

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    1. Gagan Preet Kour Marwah & Anuj Jain & Praveen Kumar Malik & Manwinder Singh & Sudeep Tanwar & Calin Ovidiu Safirescu & Traian Candin Mihaltan & Ravi Sharma & Ahmed Alkhayyat, 2022. "An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication," Mathematics, MDPI, vol. 10(21), pages 1-31, October.
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    Cited by:

    1. Gongquan Zhang & Fangrong Chang & Helai Huang & Zilong Zhou, 2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization," Mathematics, MDPI, vol. 12(13), pages 1-24, June.
    2. Xiaoning Wang & Yi Tang & Anna Grazia Quaranta, 2024. "Machine Learning-Driven Lending Decisions in Bank Consumer Finance," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 17(1), pages 1-19, January.

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      Keywords

      5G-VANET; RMDRL; QP; GOP; FR;
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