scholarly journals Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement

2018 ◽  
pp. 3-11 ◽  
Author(s):  
Ibrahim Rizqallah Alzahrani ◽  
Naeem Ramzan ◽  
Stamos Katsigiannis ◽  
Abbes Amira
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Rate Adaptation

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 318
Author(s):  
Merima Kulin ◽  
Tarik Kazaz ◽  
Eli De Poorter ◽  
Ingrid Moerman
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Performance Improvement ◽  
Quality Of Experience ◽  
Data Driven ◽  
Protocol Stack ◽  
Comprehensive Survey ◽  
Network Prediction ◽  
Network Quality

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

Improving Quality of Experience in multimedia Internet of Things leveraging machine learning on big data

2018 ◽  
Vol 86 ◽  
pp. 1413-1423 ◽  
Author(s):  
Xiaohong Huang ◽  
Kun Xie ◽  
Supeng Leng ◽  
Tingting Yuan ◽  
Maode Ma
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Quality Of Experience ◽  
Multimedia Internet Of Things

Quality of Experience Models for Multimedia Streaming

2010 ◽  
Vol 2 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Prediction Models ◽  
Multimedia Streaming ◽  
Accurate Estimation ◽  
Management Service ◽  
Subjective Data ◽  
Efficient Management ◽  
Learning Techniques

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation

2021 ◽  
Vol 48 (4) ◽  
pp. 33-36
Author(s):  
Özge Celenk ◽  
Thomas Bauschert ◽  
Marcus Eckert
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Video Streaming ◽  
Quality Of Experience ◽  
Service Providers ◽  
Early Stage ◽  
Traffic Information ◽  
Streaming Traffic ◽  
Testing Accuracy

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.

scholarly journals QoE-Aware Cross-Layer Architecture for Video Traffic over Internet

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Quality Of Experience ◽  
Rate Adaptation ◽  
Cross Layer ◽  
Foreseeable Future ◽  
Application Layer ◽  
Video Traffic ◽  
Transmission Rates ◽  
Active Video ◽  
Simulation Results

The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.

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