灵活的基于HTTP的视频自适应流在带宽突然下降时提供良好的QoE

Q2 Engineering
Nguyen Viet Hung, Trinh Dac Chien, Nam Pham Ngoc, T. Truong
{"title":"灵活的基于HTTP的视频自适应流在带宽突然下降时提供良好的QoE","authors":"Nguyen Viet Hung, Trinh Dac Chien, Nam Pham Ngoc, T. Truong","doi":"10.4108/eetinis.v10i2.2994","DOIUrl":null,"url":null,"abstract":"We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops\",\"authors\":\"Nguyen Viet Hung, Trinh Dac Chien, Nam Pham Ngoc, T. Truong\",\"doi\":\"10.4108/eetinis.v10i2.2994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.\",\"PeriodicalId\":33474,\"journal\":{\"name\":\"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eetinis.v10i2.2994\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetinis.v10i2.2994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

我们观察到,互联网上的视频流激增,特别是在Covid-19大流行期间,这可能超出了网络资源的可用性。除了升级网络基础设施之外,找到一种方法来巧妙地使流媒体系统适应网络和用户的条件,以满足客户的感知也是非常关键的。本文提出了一种新的基于HTTP的qos感知自适应流方案——ABRA,该方案可以根据网络和客户端的当前状态进行灵活的自适应。此外,我们还提出了一种可以使缓冲保持在平均高位10s以上的技术。我们限制了由于意外和不可预测的带宽变化而导致的重新缓冲现象。该算法在平均比特率下降的情况下,仍然保持后续版本的质量不变,从而提高了QoE。实验结果表明,与现有方法相比,该方法可以将QoE从7.86%提高到20.41%。与现有的解决方案相比,ABRA可以在所有缓冲区条件下提供更好的QoE分数,同时在最坏情况下保持最低的安全缓冲区级别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops
We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
4.00
自引率
0.00%
发文量
15
审稿时长
10 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信