Environment-Aware Adaptive Transmission for Adaptive Video Streaming Based on Edge Computing in High-speed rail Scenarios

Wang, Luyao; Guo, Jia; Zhu, Jinqi; Zhu, Yexuan; Wei, Yanmin; Wang, Jinao; Song, Hong‐Yeop; Gong, Xiangyang

doi:10.1109/wcnc55385.2023.10119122

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…By establishing predictive models using collected data, it can predict passenger flows and operation times in the highspeed railway system as expressed in Equ. (1). This supports proactive scheduling to achieve smoother and more efficient system operations, optimizing train timetables and enhancing transportation efficiency.…”

Section: Intelligent Task Allocationmentioning

confidence: 99%

“…The rapid advancement of global urbanization and smartification necessitates advanced information and communication technology to provide robust support for the development of intelligent rail transit systems. Through the utilization of Internet of Things (IoT) and cloud computing in the technical field, smart rail transit systems can achieve real-time data transmission and analysis, enhancing transportation efficiency and safety [1] [2]. Furthermore, the evolution of smart rail transit systems has catalyzed advancements in information and communication technology.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Wireless communication is extensively integrated into smart rail transit systems, allowing seamless interaction between rail vehicles, signal systems, dispatching centers, and passenger terminals to enable functions like realtime monitoring, navigation, and information dissemination, ensuring precise control and efficient service delivery. Additionally, high-speed wireless communication supports advanced functionalities, such as high-definition video monitoring, real-time location tracking, and intelligent scheduling commands, improving system safety and efficiency [7,8,1].…”

Section: Introductionmentioning

confidence: 99%

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Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Ren,

Song,

Long

et al. 2024

Preprint

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This paper addresses the challenges of wireless communication and intelligence in smart railway system under high-speed mobile environments, particularly the issues of Doppler effect and dynamic variability. Solutions that integrate wireless communication and edge computing is proposed. The system model based on the operational characteristics of high-speed trains is established. By analyzing historical data and employing statistical methods, the probability distribution of high-speed train operation times is studied, revealing the operational patterns across different intervals. On this foundation, an intelligent task allocation model is proposed, which optimizes the deployment of edge servers and task allocation strategies to minimize task processing costs while ensuring the stability of communication links. To address the Doppler effect and selective channel enhancement issues in high-speed mobile environments, channel prediction with periodic information is used to reduce interference and ensure the reliability of data transmission. Furthermore, intelligent scheduling system based on edge computing with predictive models is established, supporting advanced scheduling and optimizes train timetables. The algorithm is based on established scheduling rules, effectively managing the high-speed railway system and improving system efficiency. Compared with existing scheduling algorithms, the proposed method demonstrates significant advantages in reducing signal distortion, enhancing signal reliability, and optimizing resource utilization and costs.

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Section: Intelligent Task Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Ren,

Song,

Long

et al. 2024

Preprint

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Adaptive Video Task Offloading Based on Mobile Vehicle Assistance

Ma,

Wang,

et al. 2024

2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

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

Wang,

Guo,

Zhu

et al. 2024

Electronics

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In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

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Environment-Aware Adaptive Transmission for Adaptive Video Streaming Based on Edge Computing in High-speed rail Scenarios

Cited by 3 publications

References 7 publications

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Periodic Prediction Based Integrated Solutions for Wireless Communication and Edge Computing in Smart Railway Systems

Adaptive Video Task Offloading Based on Mobile Vehicle Assistance

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

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