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Measuring quality of experience for 360-degree videos in virtual reality

Science China(Information Sciences) 2020年第10期63卷 108-122页

作者： Muhammad Shahid ANWAR Jing WANG Asad ULLAH Wahab KHAN Sadique AHMAD Zesong FEI School of Information and Electronics Beijing Institute of Technology Faculty of Engineering Sciences and Technology Iqra University

In recent years, we witness dramatic growing attention in immersive media technologies like 360-degree videos and virtual reality(VR). However, measuring the quality-of-experience(QoE) for 360-degree VR videos is not a trivial task. Streaming such videos to head mounted displays(HMDs) is extremely bandwidth-demanding when compared to traditional 2D videos. In HTTP adaptive streaming, QoE tends to deteriorate significantly during fluctuating network conditions, which results in various bitrate changes and causes multiple stalling events during playback. Thus, understanding how the human visual system perceives 360-degree video with the effect of stalling and different bitrate levels becomes inevitable. In this paper, we investigate the impact of stalling on users QoE under different bitrate levels and the interaction between stalling event and bitrate level for 360-degree videos in VR. To aim this, we first build a 360-degree videos database by encoding videos in three different bitrate levels(1, 5, and 15 Mbps) with 4 K resolutions(3840×1920 pixels). We then simulate various stalling events in the videos and conduct a subjective experiment in a virtual reality environment to investigate the human responses. Finally, we use a Bayesian method to estimate and predict the QoE while measuring the quality drop owing to various stalling events and bitrate changes. Proposed solution and prediction results show a strong dependency between playback stalling and bitrate of 360-degree video in VR. Stalling always impacts the QoE of 360-degree videos, but the strength of this negative impact depends on the video bitrate level. The adverse effect of stalling events is more profound when bitrate level approaches to the high and low end, which is in close agreement with subjective opinion.

关键词： quality of experience stalling virtual reality 360-degree video

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quality of experience in Internet of Things: A Systematic Literature Review

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Journal on Internet of Things 2022年第4期4卷 263-282页

作者： Rawan Sanyour Manal Abdullah Salha Abdullah Information System Department Faculty of Computing and Information TechnologyKing Abdul Aziz UniversityJeddahSaudi Arabia

With the rapid growth of the Internet of Things paradigm,a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in various *** order to ensure that such services provide their functionalities with the expected quality,it is essential tomeasure and evaluate this quality,which can be in some cases a challenging task due to the lack of human intervention and ***,the vast majority of the quality of experience QoE works mainly address the multimedia ***,the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE *** the emerging of the new IoT technologies such as machine to machine communication and artificial intelligence,there is a crucial demand to utilize additional evaluation metrics alongside the traditional subjective and objective human factors and network quality *** this systematic review,a comprehensive survey of the QoE evaluation in IoT is *** reviews the existing quality of experience definitions,influencing factors,metrics,and *** review is concluded by identifying the current gaps in the literature and suggested some future research directions accordingly.

关键词： quality of experience QoE QoE in IoT quality of data QoD quality of service

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Recommendations for Big Data in Online Video quality of experience Assessment

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Journal of Computer and Communications 2016年第5期4卷 24-31页

作者： Ethan Court Kapilan Radhakrishnan Kemi Ademoye Stephen Hole School of Applied Computing (SOAC) University of Wales Trinity Saint David Swansea UK

Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video quality of experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.

关键词： quality of experience QoE Big Data Online Video Traffic

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QoE oriented intelligent online learning evaluation technology in B5G scenario

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Digital Communications and Networks 2024年第1期10卷 7-15页

作者： Mingzi Chen Xin Wei Peizhong Xie Zhe Zhang School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing210003China Institute of Higher Education Nanjing University of Posts and TelecommunicationsNanjing210003China

Students'demand for online learning has exploded during the post-COvID-19 pandemic ***,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always *** technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia quality of experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the *** that affect the quality of Online Learning experience(OL-QoE)become more *** get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology ***,we first formulate a general definition of OL-QoE by data analysis and experimental *** formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect ***,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE *** the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation ***,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE *** timely interventions,their OL-QoE and learning performance can be *** results verify the effectiveness oftheproposed scheme.

关键词： B5G Online learning quality of experience

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QoE-driven resource allocation for mobile IP services in wireless network

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Science China(Information Sciences) 2015年第1期58卷 222-231页

作者： FEI ZeSong XING ChengWen LI Na School of Information Science and Engineering Beijing Institute of Technology

In this study, quality of experience(QoE)-driven resource allocation for multi-applications in Internet Protocol(IP)-based wireless networks is studied. Considering that the mean opinion score(MOS) summation maximization problem is not fair to satisfy heterogeneous users' QoE with various mobile applications, we apply multi-objective optimization method to maximize each user's MOS utility. At the beginning of this work, the relationship between MOS utility and user transmission rate for three multimedia applications, that is, File Download, Internet Protocol Television, and Voice over Internet Protocol are discussed. However, the relations under diverse evaluation models are quite different and users in various mobile applications have different requirements, which make the optimization problem difficult to solve. To meet each user's minimum rate requirement,the idea of Nash bargaining solution is applied in the Hungarian-based subcarrier assignment problem. Then to simplify the power allocation problem, the concept of equivalent channel is introduced. Further by applying the tolerance membership function, we develop a fuzzy Max-Min decision model for generating an optimal power allocation solution. Simulation results demonstrate the satisfying characteristics of the proposed algorithm in terms of MOS utility and average data rate.

关键词： quality of experience mean opinion score tolerance membership function fuzziness multi-objective optimization

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A QoE-based cell range expansion scheme in heterogeneous cellular networks

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Science China(Information Sciences) 2015年第8期58卷 157-167页

作者： YUAN TingTing FEI Ze Song LI Na WANG Ni Wei XING Cheng Wen LIU Jia Kang School of Information Science and Engineering Beijing Institute of Technology The Research Institution of China Mobile

Deploying low-power nodes(LPNs) overlaid with Macro cells is a promising strategy to improve the coverage and capacity of cellular systems. Meanwhile, cell range expansion(CRE) scheme is proposed to select a proper serving base station(BS) for users between Macro e Node Bs and LPNs, and then offloading of traffic from Macro cell is made. In this paper, first, we introduce the LPNs deployment strategy and its activation criteria in the heterogeneous system. Then, a quality of experience(Qo E)-based CRE scheme to adjust the coverage area of LPNs adaptively is proposed. Different from the existing CRE schemes, the Qo E values for different users with different services are considered when the users try to select an appropriate serving BS. Finally, simulation results demonstrate that the proposed scheme can improve cell edge user experience for various services as well as overall system performance.

关键词： low power nodes heterogeneous networks cell range expansion quality of experience LTEadvanced

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LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

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Tsinghua Science and Technology 2024年第4期29卷 1219-1231页

作者： Xuan Yang James A.Esquivel Graduate School Angeles University FoundationAngeles City 2009Philippinesand also with Shandong Provincial University Laboratory for Protected HorticultureWeifang University of Science and TechnologyWeifang 262700China Graduate School Angeles University FoundationAngeles City 2009Philippines

Edge computing, which migrates compute-intensive tasks to run on the storage resources of edge devices, efficiently reduces data transmission loss and protects data privacy. However, due to limited computing resources and storage capacity, edge devices fail to support real-time streaming data query and processing. To address this challenge, first, we propose a Long Short-Term Memory (LSTM) network-based adaptive approach in the intelligent end-edge-cloud system. Specifically, we maximize the quality of experience (QoE) of users by automatically adapting their resource requirements to the storage capacity of edge devices through an event mechanism. Second, to reduce the uncertainty and non-complete adaption of the edge device towards the user’s requirements, we use the LSTM network to analyze the storage capacity of the edge device in real time. Finally, the storage features of the edge devices are aggregated to the cloud to re-evaluate the comprehensive capability of the edge devices and ensure the fast response of the user devices during the dynamic adaptation matching process. A series of experimental results show that the proposed approach has superior performance compared with traditional centralized and matrix decomposition based approaches.

关键词： quality of experience data query end-edge-cloud Long Short-Term Memory(LSTM)networks

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Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

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Digital Communications and Networks 2022年第6期8卷 1048-1058页

作者： Michael Pendo John Mahenge Chunlin Li Camilius A.Sanga Department of Informatics and Information Technology Sokoine University of AgricultureMorogoro3038Tanzania School of Computer Science and Technology Wuhan University of TechnologyWuhanHubei430063China

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient quality of experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.

关键词： Mobile edge computing quality of experience Task ofloading Communication networks Particle swarm optimizati on

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A survey of data-driven approach on multimedia QoE evaluation

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Frontiers of Computer Science 2018年第6期12卷 1060-1075页

作者： Ruochen HUANG Xin WEI Liang ZHOU Chaoping LV Hao MENG Jiefeng JIN School of Telecommunication and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communications and Networking Nanjing University of Posts and TelecommunicationsNanjing 210003China

With the development of mobile communication technology and the growth of mobile device, the requirements for user quality of experience (QoE) become higher and higher. Network operators and content providers are interested in QoE evaluation for improving users’ QoE. However, multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE. In this paper, we provide a survey of the state of the art about applying data-driven approach on QoE evaluation. Firstly, we describe the way to choose factors influencing QoE. Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users’ QoE. Finally, we describe our research work on how to evaluate QoE in imbalanced dataset.

关键词： quality of experience data-driven machine learning imbalanced dataset

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A Novel Deep Learning Method for Application Identification in Wireless Network

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China Communications 2018年第10期15卷 73-83页

作者： Jie Ren Zulin Wang School of Electronic and Information Engineering Beihang University Beijing 100191 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the quality of experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.

关键词： quality of experience application identification protocol identification deeplearning feature extraction

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