一种自注意力序列模型的视频流长期预测方法

葛云峰; 李红艳; 史可懿

doi:10.19665/j.issn1001-2400.20240202

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一种自注意力序列模型的视频流长期预测方法

信息与通信工程 | 更新时间：2024-07-23

- 一种自注意力序列模型的视频流长期预测方法
- A self-attention sequential model for long-term prediction of video streams
- 西安电子科技大学学报 2024年51卷第3期页码：88-102
- 作者机构：
  
  西安电子科技大学通信工程学院,陕西西安 710071
- 作者简介：
  
  [ "葛云峰(1999—),男,西安电子科技大学博士研究生,E-mail:[email protected]" ]
  李红艳(1966—),女,教授,E-mail:[email protected]
  [ "史可懿(1996—),男,西安电子科技大学博士研究生,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(61931017)
- DOI：10.19665/j.issn1001-2400.20240202
  中图分类号： TN915.03
- 纸质出版日期：2024-06-20，
  
  网络出版日期：2024-03-13，
  
  收稿日期：2023-06-16，
- 稿件说明：
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葛云峰, 李红艳, 史可懿. 一种自注意力序列模型的视频流长期预测方法[J]. 西安电子科技大学学报, 2024,51(3):88-102.

Yunfeng GE, Hongyan LI, Keyi SHI. A self-attention sequential model for long-term prediction of video streams[J]. Journal of Xidian University, 2024,51(3):88-102.
葛云峰, 李红艳, 史可懿. 一种自注意力序列模型的视频流长期预测方法[J]. 西安电子科技大学学报, 2024,51(3):88-102. DOI： 10.19665/j.issn1001-2400.20240202.

Yunfeng GE, Hongyan LI, Keyi SHI. A self-attention sequential model for long-term prediction of video streams[J]. Journal of Xidian University, 2024,51(3):88-102. DOI： 10.19665/j.issn1001-2400.20240202.

摘要

视频流量预测是实现传输带宽精准分配和提高互联网业务服务质量的关键技术。然而视频流量固有的高速率变异性、长期依赖性和短期依赖性使得其难以快速、精准、长期预测

具体表现为:① 现有预测序列依赖关系的模型复杂度高;② 预测模型失效快。针对视频流精准预测问题

提出了画面组帧结构特征嵌入的自注意力序列模型。自注意力序列模型对离散数据非线性关系的建模能力强

基于视频帧的特点和相关分析的发现

首次将时间序列自注意力模型应用于视频流量长期预测。现有时间序列自注意力模型无法对视频帧的类别特征有效表示

通过引入基于画面组帧结构嵌入层

将画面组帧结构信息有效嵌入时间序列

提升模型的准确度。结果表明

所提基于画面组帧结构特征嵌入的自注意力序列模型相比于现有的长短期记忆网络模型、卷积神经网络模型等

推理速度快

预测精度在平均绝对误差指标上至少降低约32%。

Abstract

Video traffic prediction is a key technology to achieve accurate transmission bandwidth allocation and improve the quality of the Internet service.However

the inherent high rate variability

long-term dependence and short-term dependence of video traffic make it difficult to make a quick

accurate and long-term prediction:because existing models for predicting sequence dependencies have a high complexity and prediction models fail quickly.Aiming at the problem of long-term prediction of video streams

a sequential self-attention model with frame structure feature embedding is proposed.The sequential self-attention model has a strong modeling ability for the nonlinear relationship of discrete data.Based on the difference of correlation between video frames

this paper applies the time series self-attention model to the long-term prediction of video traffic for the first time.The existing time series self-attention model cannot effectively represent the category features of video frames.By introducing an embedding layer based on the frame structure

the frame structure information is effectively embedded into the time series to improve the accuracy of the model.The results show that

compared with the existing long short-term memory network model and convolutional neural network model

the proposed sequential self-attention model based on frame structure feature embedding has a fast inference speed

and that the prediction accuracy is reduced by at least 32% in the mean absolute error.

关键词

预测时间序列分析网络管理视频流

Keywords

forecastingtime series analysisnetwork managementvideo streaming

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