Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology
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, l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/700 |
| _version_ | 1797623860106887168 |
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| author | Adel A. Ahmed Sharaf J. Malebary Waleed Ali Omar M. Barukab |
| author_facet | Adel A. Ahmed Sharaf J. Malebary Waleed Ali Omar M. Barukab |
| author_sort | Adel A. Ahmed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:34:48Z |
| format | Article |
| id | doaj.art-deba395c36e44faeb4b9b253d3d22f45 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:34:48Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-deba395c36e44faeb4b9b253d3d22f452023-11-16T17:23:21ZengMDPI AGMathematics2227-73902023-01-0111370010.3390/math11030700Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication TechnologyAdel A. Ahmed0Sharaf J. Malebary1Waleed Ali2Omar M. Barukab3Information Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi ArabiaInformation Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi ArabiaInformation Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi ArabiaInformation Technology Department, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 25729, Saudi ArabiaVehicles 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.https://www.mdpi.com/2227-7390/11/3/7005G-VANETRMDRLQPGOPFR |
| spellingShingle | Adel A. Ahmed Sharaf J. Malebary Waleed Ali Omar M. Barukab Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology Mathematics 5G-VANET RMDRL QP GOP FR |
| title | Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology |
| title_full | Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology |
| title_fullStr | Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology |
| title_full_unstemmed | Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology |
| title_short | Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology |
| title_sort | smart traffic shaping based on distributed reinforcement learning for multimedia streaming over 5g vanet communication technology |
| topic | 5G-VANET RMDRL QP GOP FR |
| url | https://www.mdpi.com/2227-7390/11/3/700 |
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