Quality Of Experience
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The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
Multimedia Content Understanding, Modeling, Management, andRetrieval• Multimedia meta-modeling techniques• Multimedia storage systems, databases, and retrieval• Multimedia data segmentation• Image, audio, video, genre clustering & classification• Video summarization and story generation• Speaker identification, recognition, and location• Object, event, emotion, text detection and recognition• Mosaic, video panorama and background generation• Multimedia semantics, ontologies, annotation, concept detection andlearning• Personalization and user preferences• 3D and depth information• Viewer perception, emotion analysis and visual attention• Multimedia datasets and open source code for research• Multimedia recommender systems• Fake multimedia detectionMultimedia Interfaces• Multimedia information visualization and interactive systems• Multimodal user interfaces: design, engineering, modality-abstractions,etc.• Multimedia tools for authoring, analyzing, editing
The Eleventh International Conference on Quality of Multimedia Experience will bring together leading experts from academia and industry to present and discuss current and future research on multimedia quality, quality of experience (QoE) and user experience (UX). This way, it will contribute towards an integrated view on QoE and UX, and foster the exchange between the so-far distinct communities.
Intelligent Communication Systems, IoT, Multimedia and Systems, Image Processing, Intelligent Signal Processing, AI, Big Data, VLSI, Circuits and Systems, and Emerging Technologies in Signal Processing and Communication.
IEEE ISCT International Symposium on Consumer Technologies (former International Symposium on Consumer Electronics) is the established forum for innovative research in all technology areas of consumer electronics. The theme of ISCT 2018 is “Consumer Technologies in 10 years”.Paper contributions are sought in but are not limited to following areas:Internet of Things and Internet of Everywhere (IoT)Consumer Healthcare Systems (CHS)Energy Management of CE Hardware and Software Systems (EMC)Application-Specific CE for Smart Cities (SMC)Artificial Intelligence in Consumer Technologies (AIC)Consumer Technologies Quality and Testing (CTQ)Telecom, RF, Wireless, and Network Technologies (WNT)AV Systems, Image and Video, and Cameras and Acquisition (AVS)CE Sensors and MEMS (CSM)Smartphone and Mobile Device Technologies (MDT)Entertainment, Gaming, and Virtual and Augmented Reality (EGV)Other Technologies Related with CE (MIS)Education in Consumer Technologies Area (EDU)
No periodicals are currently tagged "Quality Of Experience"
IEEE Transactions on Emerging Topics in Computing, None
In the era of information, the green services of content-centric IoT are expected to offer users the better satisfaction of Quality of Experience (QoE) than that in a conventional IoT. Nevertheless, the network traffic and new demands from IoT users increase along with the promising of the content- centric computing system. Therefore, the satisfaction of QoE will become the major ...
IEEE Access, None
5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the ...
China Communications, 2018
The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience (SQoE) Key Quality Indicators (KQIs) to reflect accessibility, integrality, and maintainability of networks. Then, we ...
China Communications, 2018
Quality of experience (QoE), which is very critical for the experience of users in wireless networks, has been extensively studied. However, due to different human perceptions, quantifying the effective capacity of wireless network subject to diverse QoE is very difficult, which leads to many new challenges regarding QoE guarantees in wireless networks. In this paper, we formulate the QoE guarantees ...
2018 IEEE International Smart Cities Conference (ISC2), 2018
Smart City requires an intelligent and efficient network to provide services with good quality-of-experience (QoE). In this paper, we propose to develop a secure and distributed network QoE measurement for smart networks in Smart Cities. Network measurement capability has been updated gradually as the network technology progresses. For example, software-defined network will enable efficient monitoring and control of the core ...
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In the era of information, the green services of content-centric IoT are expected to offer users the better satisfaction of Quality of Experience (QoE) than that in a conventional IoT. Nevertheless, the network traffic and new demands from IoT users increase along with the promising of the content- centric computing system. Therefore, the satisfaction of QoE will become the major challenge in the content-centric computing system for IoT users. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the qualities of the IoT concerning both network and users. The value of QoE does not only refer to the network cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture the influence factors from network cost and services for IoT users based on IoT conditions. Specially, we mainly focus on the issues of cache allocation and transmission rate. Under this content-centric IoT, aiming to allocate the cache capacity among content- centric computing nodes and handle the transmission rates under a constrained total network cost and MOS for the whole IoT, we devote our efforts to the following two aspects. First, we formulate the QoE as a green resource allocation problem under the different transmission rate to acquire the best QoE. Then, in the basis of the node centrality, we will propose a suboptimal dynamic approach, which is suitable for IoT with content delivery frequently. Furthermore, we present a green resource allocation algorithm based on Deep Reinforcement Learning (DRL) to improve accuracy of QoE adaptively. Simulation results reveal that our proposals could achieve high QoE performance for content-centric IoT.
5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly—outage and sudden hype in traffic activity that may result in congestion—detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)’s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes L-layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)—notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector.
The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience (SQoE) Key Quality Indicators (KQIs) to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators (rKPIs) as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self- organizing map (SOM) and k-medoids is used for clustering them into different types. We apply this framework to online anomaly detection in Cellular Networks, named SQoE-driven Anomaly Detection and Cause Location System (SQoE- ADCL). Our experiments with real 4G data show that besides fast online detection, SQoE-ADCL makes a better soft decision instead of a traditional hard decision. Furthermore, it is also a general way of being applied to other similar applications in big data.
Quality of experience (QoE), which is very critical for the experience of users in wireless networks, has been extensively studied. However, due to different human perceptions, quantifying the effective capacity of wireless network subject to diverse QoE is very difficult, which leads to many new challenges regarding QoE guarantees in wireless networks. In this paper, we formulate the QoE guarantees model for cellular wireless networks. Based on the model, we convert the effective capacity maximization problem into the equivalent convex optimization problem. Then, we develop the optimal QoE- driven power allocation scheme, which can maximize the effective capacity. The obtained simulation results verified our proposed power allocation scheme, showing that the effective capacity can be significantly increased compared with that of traditional QoE guarantees based schemes.
Smart City requires an intelligent and efficient network to provide services with good quality-of-experience (QoE). In this paper, we propose to develop a secure and distributed network QoE measurement for smart networks in Smart Cities. Network measurement capability has been updated gradually as the network technology progresses. For example, software-defined network will enable efficient monitoring and control of the core network. However, end-to- end network QoE measurement requires distributed approaches from the user side. In our proposed measurement framework, a traffic measurement agent is deployed in the last-hop gateway. The gateway is equipped with new features, i.e., encrypted packet classifier, traffic prediction, and user quality-of- service (QoS) to QoE mappings. Since all measurement process is done at the gateway, end user devices are separated from the entire process. Thus security can be provided by the proposed measurement framework. In addition to the framework, we demonstrated a efficient learning approach to develop the traffic prediction scheme and the QoS to QoE mapping scheme. Experiments results demonstrated that the developed schemes are applicable to a distributed network QoS measurement framework for Smart City network services.
Fog computing is an advanced technique to decrease latency and network congestion, and provide economical gains for Internet of Things (IoT) networks. In this paper, we investigate the computing resource allocation problem in three-layer fog computing networks. We first formulated the resource allocation problem as a double two-sided matching optimization problem. Then, we propose a double-matching strategy for the resource allocation problem in fog computing networks based on cost efficiency, which is derived by analysing the utility and cost in fog computing networks. The proposed double-matching strategy is an extension of the deferred acceptance algorithm from two-side matching to three-side matching. Numerical results show that high cost efficiency performance can be achieved by adopting the proposed strategy. Furthermore, by using the proposed strategy, the three participants in the fog computing networks could achieve stable results that each participant cannot change its paired partner unilaterally for more cost efficiency.
Bandwidth requirements of both wireless and wired clients in access networks continue to increase rapidly, primarily due to the growth of video traffic. Application awareness can be utilized in access networks to optimize quality of experience (QoE) of end clients. In this study, we utilize information at the client-side application (e.g., video resolution) to achieve superior resource allocation that improves user QoE. We emphasize optimizing QoE of the system rather than quality of service (QoS), as user satisfaction directly relies on QoE and optimizing QoS does not necessarily optimize QoE, as shown in this study. We propose application-aware resource-allocation schemes on an Ethernet passive optical network (EPON), which supports wireless (utilizing orthogonal frequency division multiple access) and wired clients running video-conference applications. Numerical results show that the application- aware resource-allocation schemes improve QoE for video-conference applications for wired and wireless clients.
This paper proposes a method for optimal classification of voice packets to enhance the quality of voice communications over priority-enabled networks when poor transmission conditions occur. Either high or low priority is assigned to each packet according to the relevance of its payload (voice segment) for the voice intelligibility. Then, in case of constrained networking conditions, by discarding first the voice packets of lower importance, the network always delivers those segments that most contribute to the perceptual quality. The proposed method is based on a dynamic programming optimisation algorithm that finds the optimal subset of m high priority voice segments in each utterance of size n > m. Such optimal subset minimizes the reconstruction distortion over all possible subsets with the same size m (i.e., the distortion incurred by a utterance reconstructed from only m segments). The simulation results show that the proposed method consistently achieves higher mean opinion scores (MOS) in comparison with non-selective packet drop under the same random network loss conditions, yielding better quality of experience (QoE) for the same packet loss rates (PLR). The priority classification algorithm is independent from error concealment methods and distortion metrics used in the optimisation process, which allows generalisation for diverse communication networks and applications.
Poor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short.
Context-aware systems are becoming increasingly popular and are widely used in many areas such as smart healthcare, digital home, and so on. The existing context-aware system frameworks mainly focus on the improvement of organization mode of the system modules, but they normally lack a quantitative and comprehensive quality index to describe and evaluate the overall system performance. In this paper, QoX is innovatively defined as a comprehensive quality index that combines quality of device (QoD), quality of context (QoC), quality of service (QoS) and quality of experience (QoE). Meanwhile, QoX is integrated into the context-aware system and a context-aware system framework based on QoX is proposed, which provides an effective model for rational use of context and optimal system design. Besides, by studying the relation among QoX related indicators, systems can actively adjust various preset thresholds and rules according to the changes of users' needs and environmental conditions, which would make systems more reliable and adaptive and provide users with personalized and intelligent services.
No standards are currently tagged "Quality Of Experience"