摘 要：
随着互联网技术的迅猛发展，实时视频传输已成为现代通信的重要组成部分，然而网络延迟问题严重制约了用户体验和系统性能的提升为此，本研究聚焦于网络延迟优化技术在实时视频传输中的应用，旨在通过改进数据传输机制以降低延迟并提高传输质量研究中采用了一种基于自适应带宽预测与动态路径选择的混合优化策略，结合机器学习算法对网络状态进行实时监测与预测，并通过引入多路径传输技术实现数据流的智能分配实验结果表明，该方法能够有效减少约30%的平均传输延迟，同时显著提升了视频流的稳定性和清晰度此外，本研究还提出了一种新型的延迟补偿机制，能够在高延迟或不稳定网络环境下进一步优化用户体验综上所述，本研究不仅为实时视频传输提供了高效的延迟优化方案，还为未来相关领域的技术发展奠定了理论与实践基础其创新点在于将机器学习与多路径传输相结合，实现了智能化、动态化的网络资源调度，从而大幅改善了实时视频传输的整体性能
关键词：实时视频传输；网络延迟优化；自适应带宽预测；多路径传输；机器学习算法
Research on the Application of Network Latency Optimization Techniques in Real-Time Video Transmission
Abstract: With the rapid development of Internet technology, real-time video transmission has become a crucial component of modern communication. However, network latency remains a significant constraint on user experience and system performance enhancement. This study focuses on the application of network latency optimization techniques in real-time video transmission, aiming to reduce latency and improve transmission quality by refining data transfer mechanisms. An integrated optimization strategy based on adaptive bandwidth prediction and dynamic path selection was employed, incorporating machine learning algorithms for real-time monitoring and forecasting of network conditions, along with multipath transmission technology to enable intelligent data flow allocation. Experimental results demonstrate that this approach effectively reduces average transmission latency by approximately 30%, while significantly enhancing the stability and clarity of video streams. Furthermore, this research proposes a novel delay compensation mechanism designed to optimize user experience under high-latency or unstable network environments. In summary, this study not only provides an efficient latency optimization solution for real-time video transmission but also lays a theoretical and practical foundation for future technological advancements in related fields. Its innovation lies in the integration of machine learning with multipath transmission, achieving intelligent and dynamic network resource scheduling, thereby substantially improving the overall performance of real-time video transmission.
Keywords: Real-Time Video Transmission; Network Latency Optimization; Adaptive Bandwidth Prediction; Multi-Path Transmission; Machine Learning Algorithm
目 录
1绪论 1
1.1网络延迟优化的研究背景与意义 1
1.2实时视频传输中的技术挑战分析 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2实时视频传输中的延迟问题分析 2
2.1视频传输延迟的定义与分类 2
2.2延迟对实时视频质量的影响评估 3
2.3主要延迟来源及其特性分析 4
2.4延迟优化的技术需求与目标设定 4
2.5数据驱动的延迟问题建模方法 5
3网络延迟优化的关键技术研究 5
3.1基于QoS的网络资源分配策略 5
3.2路由优化算法在延迟控制中的应用 6
3.3缓冲区管理技术对延迟的改进作用 6
3.4边缘计算在降低延迟中的优势分析 7
3.5多路径传输技术的延迟优化效果 7
4延迟优化技术在实时视频传输中的实践应用 8
4.1实时视频传输系统的架构设计 8
4.2延迟优化技术的实际部署方案 8
4.3典型应用场景下的性能测试与分析 9
4.4延迟优化效果的量化评估指标 9
4.5技术应用中面临的挑战与改进方向 10
结论 10
参考文献 12
致 谢 13
