Environment-aware streaming media transmission method in high-speed mobile networks

IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS
Jia Guo, Jinqi Zhu, Xiang Li, Bowen Sun, Qian Gao, Weijia Feng
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引用次数: 0

Abstract

With technological advancements, high-speed rail has emerged as a prevalent mode of transportation. During travel, passengers exhibit a growing demand for streaming media services. However, the high-speed mobile networks environment poses challenges, including frequent base station handoffs, which significantly degrade wireless network transmission performance. Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers' media experiences are key research priorities. To address these issues, we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness (ACOTM-EA) tailored for high-speed rail streaming media. Within this framework, we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes. Additionally, we introduce a proactive base station handoff strategy to minimize handoff-related disruptions and optimize resource distribution across adjacent base stations. Moreover, this study presents a wireless resource allocation approach based on an enhanced genetic algorithm, coupled with an adaptive bitrate selection mechanism, to maximize passenger Quality of Experience (QoE). To evaluate the proposed method, we designed a simulation experiment and compared ACOTM-EA with established algorithms. Results indicate that ACOTM-EA improves throughput by 11% and enhances passengers' media experience by 5%.
高速移动网络中环境感知流媒体传输方法
随着科技的进步,高速铁路已经成为一种普遍的交通方式。在旅行中,乘客对流媒体服务的需求日益增长。然而,高速移动网络环境带来了挑战,包括频繁的基站切换,这大大降低了无线网络的传输性能。提高高速移动网络的传输效率,优化无线资源的时空分配,以增强乘客的媒体体验是研究的重点。为了解决这些问题,我们提出了一种针对高铁流媒体的环境意识自适应跨层优化传输方法(ACOTM-EA)。在这个框架内,我们开发了一个信道质量预测模型,利用卡尔曼滤波和一种算法来识别数据包丢失的原因。此外,我们引入了一种主动的基站切换策略,以最大限度地减少与切换相关的中断,并优化相邻基站之间的资源分配。此外,本研究提出了一种基于增强型遗传算法的无线资源分配方法,结合自适应比特率选择机制,以最大限度地提高乘客体验质量(QoE)。为了评估所提出的方法,我们设计了一个仿真实验,并将ACOTM-EA与已有算法进行了比较。结果表明,ACOTM-EA提高了11%的吞吐量,提高了5%的乘客媒体体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Communications and Networks
Digital Communications and Networks Computer Science-Hardware and Architecture
CiteScore
12.80
自引率
5.10%
发文量
915
审稿时长
30 weeks
期刊介绍: Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.
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