Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks最新文献

On the Bitrate Adaptation of Shared Media Experience Services 共享媒体体验服务的比特率适配研究

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098608

Argyrios G. Tasiopoulos, Ray S. Atarashi, I. Psaras, G. Pavlou

{"title":"On the Bitrate Adaptation of Shared Media Experience Services","authors":"Argyrios G. Tasiopoulos, Ray S. Atarashi, I. Psaras, G. Pavlou","doi":"10.1145/3098603.3098608","DOIUrl":"https://doi.org/10.1145/3098603.3098608","url":null,"abstract":"In Shared Media Experience Services (SMESs), a group of people is interested in streaming consumption in a synchronised way, like in the case of cloud gaming, live streaming, and interactive social applications. However, group synchronisation comes at the expense of other Quality of Experience (QoE) factors due to both the dynamic and diverse network conditions that each group member experiences. Someone might wonder if there is a way to keep a group synchronised while maintaining the highest possible QoE for each one of its members. In this work, at first we create a Quality Assessment Framework capable of evaluating different SMESs improvement approaches with respect to traditional metrics like media bitrate quality, playback disruption, and end user desynchronisation. Secondly, we focus on the bitrate adaptation for improving the QoE of SMESs, as an incrementally deployable end user triggered approach, and we formulate the problem in the context of Adaptive Real Time Dynamic Programming (ARTDP). Finally, we develop and apply a simple QoE aware bitrate adaptation mechanism that we compare against youtube live-streaming traces to find that it improves the youtube performance by more than 30%.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

On Active Sampling of Controlled Experiments for QoE Modeling QoE建模中控制实验的主动抽样研究

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098609

Muhammad Jawad Khokhar, Nawfal Abbassi Saber, Thierry Spetebroot, C. Barakat

{"title":"On Active Sampling of Controlled Experiments for QoE Modeling","authors":"Muhammad Jawad Khokhar, Nawfal Abbassi Saber, Thierry Spetebroot, C. Barakat","doi":"10.1145/3098603.3098609","DOIUrl":"https://doi.org/10.1145/3098603.3098609","url":null,"abstract":"For internet applications, measuring, modeling and predicting the quality experienced by end users as a function of network conditions is challenging. A common approach for building application specific Quality of Experience (QoE) models is to rely on controlled experimentation. For accurate QoE modeling, this approach can result in a large number of experiments to carry out because of the multiplicity of the network features, their large span (e.g., bandwidth, delay) and the time needed to setup the experiments themselves. However, most often, the space of network features in which experimentations are carried out shows a high degree of uniformity in the training labels of QoE. This uniformity, difficult to predict beforehand, amplifies the training cost with little or no improvement in QoE modeling accuracy. So, in this paper, we aim to exploit this uniformity, and propose a methodology based on active learning, to sample the experimental space intelligently, so that the training cost of experimentation is reduced. We prove the feasibility of our methodology by validating it over a particular case of YouTube streaming, where QoE is modeled both in terms of interruptions and stalling duration.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122889776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Designing a Survey Tool for Monitoring Enterprise QoE 企业QoE监测调查工具的设计

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098610

Kathrin Borchert, Matthias Hirth, T. Zinner, A. Göritz

引用次数: 4

Server and Content Selection for MPEG DASH Video Streaming with Client Information 带有客户端信息的MPEG DASH视频流的服务器和内容选择

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098607

Florian Wamser, Steffen Höfner, Michael Seufert, P. Tran-Gia

引用次数: 1

Perceived Performance of Top Retail Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE 野外顶级零售网页的感知表现:来自大规模众包的QoE洞察

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098606

Qingzhu Gao, Prasenjit Dey, P. Ahammad

{"title":"Perceived Performance of Top Retail Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE","authors":"Qingzhu Gao, Prasenjit Dey, P. Ahammad","doi":"10.1145/3098603.3098606","DOIUrl":"https://doi.org/10.1145/3098603.3098606","url":null,"abstract":"Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the Speed of a page. In this paper we present SpeedPerception1, a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our SpeedPerception study using Internet Retailer Top 500 (IR 500) websites [3], we found that commonly used navigation metrics such as onLoad and Time To First Byte (TTFB) fail (less than 60% match) to represent majority human perception when comparing the speed of two webpages. We present a simple 3-variable-based machine learning model that explains the majority end-user choices better (with 87 ± 2% accuracy). In addition, our results suggest that the time needed by end-users to evaluate relative perceived speed of webpage is far less than the time of its visualComplete event.","PeriodicalId":164573,"journal":{"name":"Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132659978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

PAIN: A Passive Web Speed Indicator for ISPs PAIN:互联网服务提供商的被动网络速度指示器

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098605

Martino Trevisan, I. Drago, M. Mellia

引用次数: 9

A QoE Perspective on HTTP/2 Server Push HTTP/2服务器推送的QoE视角

Proceedings of the Workshop on QoE-based Analysis and Management of Data Communication Networks Pub Date : 2017-08-21 DOI: 10.1145/3098603.3098604

T. Zimmermann, Benedikt Wolters, O. Hohlfeld

引用次数: 12