Lamine Amour

Bio: Lamine Amour is an academic researcher from University of Paris-Est. The author has contributed to research in topics: Quality of experience & Mean opinion score. The author has an hindex of 4, co-authored 9 publications receiving 37 citations. Previous affiliations of Lamine Amour include Paris 12 Val de Marne University.

A Hierarchical Classification Model of QoE Influence Factors

Abstract: Quality of Service (QoS) optimization are not sufficient to ensure users needs. That’s why, operators are investigating a new concept called Quality of Experience (QoE), to evaluate the real quality perceived by users. This concept becomes more and more important, but still hard to estimate. This estimation can be influenced by a lot of factors called: Quality of Experience Influence Factors (QoE IFs). In this work, we survey and review existing approaches to classify QoE IFs. Then, we present a new modular and extensible classification architecture. Finally, regarding the proposed classification, we evaluate some QoE estimation approaches to highlight the fact that categories do not affect in the same the user perception.

An improved QoE estimation method based on QoS and affective computing

Abstract: With the massive uses of the video over the world in the last decade, the user perception, commonly called Quality of Experience (QoE) metric; has become the one of the most important topics for the Network services Providers (NsP) and Content service Providers (CsP). In this paper, we present a new QoE estimation method on the client side using Machine Learning methods (ML) based on subjective assessment in a controlled-laboratory environment. The major novel contribution of this study is the combination of Quality of Service (QoS) parameters and Affective Computing (facial expression) to estimate the Mean Opinion Score (MOS) for HTTP YouTube content. An evaluation using a collected subjective dataset indicates that combining QoS and Affective computing provides better prediction performance.

Building a Large Dataset for Model-based QoE Prediction in the Mobile Environment

Abstract: The tremendous growth in video services, specially in the context of mobile usage, creates new challenges for network service providers: How to enhance the user's Quality of Experience (QoE) in dynamic wireless networks (UMTS, HSPA, LTE/LTE-A). The network operators use different methods to predict the user's QoE. Generally to predict the user's QoE, methods are based on collecting subjective QoE scores given by users. Basically, these approaches need a large dataset to predict a good perceived quality of the service. In this paper, we setup an experimental test based on crowdsourcing approach and we build a large dataset in order to predict the user's QoE in mobile environment in term of Mean Opinion Score (MOS). The main objective of this study is to measure the individual/global impact of QoE Influence Factors (QoE IFs) in a real environment. Based on the collective dataset, we perform 5 testing scenarios to compare 2 estimation methods (SVM and ANFIS) to study the impact of the number of the considered parameters on the estimation. It became clear that using more parameters without any weighing mechanisms can produce bad results.

Quality Estimation Framework for Encrypted Traffic (Q2ET)

Abstract: In the coming years, the development of the Internet of Things (IoT) will have relevance for transport, environment, health care, smart cities and also multimedia services (Multimedia Internet of Things (MIoT)). Nowadays, many ISP (Internet Service Provider) encrypt the data to make it secure during the transmission. However, it imposes some obstacles for the NSP (Network Service Provider) because of the lack of visibility for operators into network traffic. To resolve these issues, we proposed the Quality Estimation Framework for Encrypted Traffic (Q2ET) containing a classification module and a QoE assessment module. The first module inherited from our previous research works to classify the encrypted network traffic using CNN (Convolutional Neural Network). The second one applies the objective and subjective methods based on the statistical analysis and machine learning methods that combine application and network parameters to calculate user's QoE (Quality of Experience) in terms of MOS (Mean Opinion Score). The Q2ET allows the NSP to monitor the user's QoE to take the appropriate decisions when the QoE degradation happens in the network systems.

An Open Source Platform for Perceived Video Quality Evaluation

Abstract: To ensure the best multimedia service quality in order to well address users' expectations, a new concept named Quality of Experience (QoE) has appeared. Two methods can be used to evaluate the user satisfaction, a subjective one and an objective one. The subjective approach is based on measured real data. The problem is there is no dataset large enough and can be used to well evaluate the QoE. In this, work we present our approach to build a data set for subjective evaluation based on a categorization approach and open source software.

Estimation of the Quality of Experience During Video Streaming From Facial Expression and Gaze Direction

Abstract: This article investigates the possibility to estimate the perceived Quality of Experience (QoE) automatically and unobtrusively by analyzing the face of the consumer of video streaming services, from which facial expression and gaze direction are extracted. If effective, this would be a valuable tool for the monitoring of personal QoE during video streaming services without asking the user to provide feedback, with great advantages for service management. Additionally, this would eliminate the bias of subjective tests and would avoid bothering the viewers with questions to collect opinions and feedback. The performed analysis relies on two different experiments: i) a crowdsourcing test, where the videos are subject to impairments caused by long initial delays and re-buffering events; ii) a laboratory test, where the videos are affected by blurring effects. The facial Action Units (AU) that represent the contractions of specific facial muscles together with the position of the eyes’ pupils are extracted to identify the correlation between perceived quality and facial expressions. An SVM with a quadratic kernel and a k-NN classifier have been tested to predict the QoE from these features. These have also been combined with measured application-level parameters to improve the quality prediction. From the performed experiments, it results that the best performance is obtained with the k-NN classifier by combining all the described features and after training it with both the datasets, with a prediction accuracy as high as 93.9% outperforming the state of the art achievements.

Quality of Experience in 6G Networks: Outlook and Challenges

Abstract: In this paper, we discuss the critical characteristics of user experience in sixth generation (6G) cellular networks. We first describe cellular networks’ evolution through 5G and then discuss the enabling technologies and projected services in 6G networks. We note that these networks are markedly centered around expanded intelligence, end-to-end resource and topology synchronization, and the intrinsic support to low-latency, high-bandwidth communication. These capabilities make context-rich, cyberphysical user experiences viable. It thereby becomes necessary to define and identify the role of quality of experience in 6G networks, especially when it comes to network management. We elaborate on these expected challenges and allude to viable opportunities in emerging technologies.

The contrast effect: QoE of mixed video-qualities at the same time

Abstract: In desktop multi-party video-conferencing videostreams of participants are delivered in different qualities, but we know little about how such composition of the screen affects the quality of experience. Do the different videostreams serve as indirect quality references and the perceived video quality is thus dependent on other streams in the same session? How is the relation between the perceived qualities of each stream and the perceived quality of the overall session? To answer these questions we conducted a crowdsourcing study, in which we gathered over 5000 perceived quality ratings of overall sessions and individual streams. Our results show a contrast effect: high quality streams are rated better when more low quality streams are co-present, and vice versa. In turn, the quality perception of an overall session can increase significantly by exchanging one low quality stream with a high quality one. When comparing the means of individual and overall ratings we can further observe that users are more critical when asked for individual streams than for an overall rating. However, the results show that while contrast effect exists, the effect is not strong enough, to optimize the experience by lowering the quality of other participants.