Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks

被引:0
作者
Huang, Chenn-Jung [1 ]
Hu, Kai-Wen [2 ]
Cheng, Hao-Wen [1 ]
Jian, Mei-En [1 ]
Tsamarah, Muhammad Inas Farras [1 ]
机构
[1] Natl Dong Hwa Univ, Dept Comp Sci & Informat Engn, Hualien Cty 974301, Shoufeng, Taiwan
[2] Lookout Inc, Taipei 110207, Taiwan
关键词
electric vehicle; V2X; bandwidth allocation; multimedia application; intelligence control; TRAVEL-TIME PREDICTION;
D O I
10.3390/s24155007
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.
引用
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页数:30
相关论文
共 48 条
  • [1] Smart Traffic Shaping Based on Distributed Reinforcement Learning for Multimedia Streaming over 5G-VANET Communication Technology
    Ahmed, Adel A.
    Malebary, Sharaf J.
    Ali, Waleed
    Barukab, Omar M.
    [J]. MATHEMATICS, 2023, 11 (03)
  • [2] Cell-free massive multiple-input multiple-output challenges and opportunities: A survey
    Ajmal, Mahnoor
    Siddiqa, Ayesha
    Jeong, Bomi
    Seo, Junho
    Kim, Dongkyun
    [J]. ICT EXPRESS, 2024, 10 (01): : 194 - 212
  • [3] Spectrum Options and Allocations for 6G: A Regulatory and Standardization Review
    Alsaedi, Wijdan K.
    Ahmadi, Hamed
    Khan, Zaheer
    Grace, David
    [J]. IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2023, 4 : 1787 - 1812
  • [4] [Anonymous], 2024, 300+ Video Marketing Statistics
  • [5] Baeza VM, 2023, Arxiv, DOI arXiv:2303.09690
  • [6] Multi-Path Transmission Protocol for Video Streaming Over Vehicular Fog Computing Environments
    Benzerogue, Sarra
    Abdelatif, Sahraoui
    Merniz, Salah
    Harous, Saad
    Khamer, Lazhar
    [J]. IEEE ACCESS, 2024, 12 : 87199 - 87216
  • [7] Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing
    Chaccour, Christina
    Soorki, Mehdi Naderi
    Saad, Walid
    Bennis, Mehdi
    Popovski, Petar
    Debbah, Merouane
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2022, 24 (02): : 967 - 993
  • [8] Joint Communication and Control for mmWave/THz Beam Alignment in V2X Networks
    Chang, Bo
    Yan, Xiaoyu
    Zhang, Lei
    Chen, Zhi
    Li, Lingxiang
    Imran, Muhammad Ali
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (13) : 11203 - 11213
  • [9] Overview and Performance Evaluation of Wi-Fi 7
    Chen C.
    Chen X.
    Das D.
    Akhmetov D.
    Cordeiro C.
    [J]. IEEE Communications Standards Magazine, 2022, 6 (02): : 12 - 18
  • [10] Speed Up VVC Intra-Coding by Learned Models and Feature Statistics
    Chen, Jiann-Jone
    Chou, Yeh-Guan
    Jiang, Chi-Shiun
    [J]. IEEE ACCESS, 2023, 11 : 124609 - 124623