Application identification for virtual reality video with feature analysis and machine learning technique. Liu, X., Chen, X., Wang, Y., & Liu, Y. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 262:336–346, 2019. tex.author_keywords: Application identification; Machine learning; Statistical feature; VR video application tex.document_type: Conference Paper tex.source: Scopus![Application identification for virtual reality video with feature analysis and machine learning technique [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Immersive media services such as Virtual Reality (VR) video have attracted more and more attention in recent years. They are applications that typically require large bandwidth, low latency, and low packet loss ratio. With limited network resources in wireless network, video application identification is crucial for optimized network resource allocation, Quality of Service (QoS) assurance, and security management. In this paper, we propose a set of statistical features that can be used to distinguish VR video from ordinary video. Six supervised machine learning (ML) algorithms are explored to verify the identification performance for VR video application using these features. Experimental results indicate that the proposed features combined with C4.5 Decision Tree algorithm can achieve an accuracy of 98.6
@article{Liu2019336,
title = {Application identification for virtual reality video with feature analysis and machine learning technique},
volume = {262},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060700437&doi=10.1007%2f978-3-030-06161-6_33&partnerID=40&md5=b7e365729cea754d8fcb4896c3a7dd9a},
doi = {10.1007/978-3-030-06161-6_33},
abstract = {Immersive media services such as Virtual Reality (VR) video have attracted more and more attention in recent years. They are applications that typically require large bandwidth, low latency, and low packet loss ratio. With limited network resources in wireless network, video application identification is crucial for optimized network resource allocation, Quality of Service (QoS) assurance, and security management. In this paper, we propose a set of statistical features that can be used to distinguish VR video from ordinary video. Six supervised machine learning (ML) algorithms are explored to verify the identification performance for VR video application using these features. Experimental results indicate that the proposed features combined with C4.5 Decision Tree algorithm can achieve an accuracy of 98.6},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
author = {Liu, X. and Chen, X. and Wang, Y. and Liu, Y.},
year = {2019},
note = {tex.author\_keywords: Application identification; Machine learning; Statistical feature; VR video application
tex.document\_type: Conference Paper
tex.source: Scopus},
keywords = {\#nosource},
pages = {336--346},
}
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