Adaptive Quality-of-Service Allocation Scheme for Improving Video Quality over a Wireless Network

IF 2 4区 计算机科学 Q2 Computer Science Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI:10.32604/iasc.2022.020482
R. Alsaqour, Ammar Hadi, Maha S. Abdelhaq
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Abstract

The need to ensure the quality of video streaming transmitted over wireless networks is growing every day. Video streaming is typically used for applications that are sensitive to poor quality of service (QoS) due to insufficient bandwidth, packet loss, or delay. These challenges hurt video streaming quality since they affect throughput and packet delivery of the transmitted video. To achieve better video streaming quality, throughput must be high, with minimal packet delay and loss ratios. A current study, however, found that the adoption of the adaptive multiple TCP connections (AM-TCP), as a transport layer protocol, improves the quality of video streaming by increasing throughput and lowering packet delay and loss on high QoS wireless networks. The main objective of this study is to develop a Quality of Service (QoS) method for enhancing video streaming quality over wireless networks. For that, this paper proposes a bandwidth aggregation adaptive multiple TCP-connection (BAM-TCP) scheme to enhance the video streaming quality. BAM-TCP combines AM-TCP with a bandwidth aggregation (BAG) method to aggregate bandwidth from numerous connections. Simulation experiments demonstrate that the BAM-TCP method enhances throughput while minimizing packet loss ratio and end-to-end delay.
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提高无线网络视频质量的自适应业务质量分配方案
确保通过无线网络传输的视频流质量的需求日益增长。视频流通常用于带宽不足、丢包或延迟等对服务质量(QoS)较差敏感的应用。这些挑战会影响视频流的质量,因为它们会影响传输视频的吞吐量和分组传输。为了获得更好的视频流质量,吞吐量必须高,数据包延迟和丢包率必须最小。然而,目前的一项研究发现,采用自适应多TCP连接(AM-TCP)作为传输层协议,在高QoS无线网络上通过增加吞吐量和降低数据包延迟和丢失来提高视频流的质量。本研究的主要目标是开发一种服务质量(QoS)方法,以提高无线网络上视频流的质量。为此,本文提出了一种带宽聚合自适应多tcp连接(BAM-TCP)方案来提高视频流的质量。BAM-TCP将AM-TCP与带宽聚合(BAG)方法结合起来,从多个连接中聚合带宽。仿真实验表明,该方法在最大限度地降低丢包率和端到端延迟的同时提高了吞吐量。
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来源期刊
Intelligent Automation and Soft Computing
Intelligent Automation and Soft Computing 工程技术-计算机:人工智能
CiteScore
3.50
自引率
10.00%
发文量
429
审稿时长
10.8 months
期刊介绍: An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.
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