An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience

The scope of this work is to present a holistic approach in quality of service (QoS) and quality of experience (QoE) characterization and prediction in modern mobile communication networks. Analytically, multi radio access technologies have been deployed in order to deliver mobile services to quality demanded consumers. Quality of Experience (QoE) parameters describe the End-to-End (E2E) quality as experienced by the mobile users. These parameters are difficult to be measured and quantified. System Quality of Service (SQoS) parameters are metrics that are closely related to the network status, and defined from the viewpoint of the service provider rather than the service user. Moreover, E2E Service Quality of Service (ESQoS) parameters describe the QoS of the services and they are obtained directly from the QoE parameters by mapping them into parameters more relevant to network operators, service providers and mobile users. A useful technique for mobile network planning and optimization is to build reliable quality estimation models for mobile voice and video telephony service.

The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2–5 times for the criterion of the number of customers who require high quality of experience of the service.

The coevolution of 5G technological development and the growing market toward the 5G era has generated an innovation enhancement pattern. Identifying this pattern will reveal the 5G services in demand in the market and the infrastructure required for improving the 5G era. Herein, the patterns of 5G technological innovation enhancements are identified as expected in the Indonesian market. We use a novel approach that combines the users' accumulated experience of mobile technology and market expectation. Two types of questionnaires are developed for this purpose: the first determines the quality of experience (QoE) parameters of 4G compared to the preceding 3G and 2G generations and the second analyzes the expectation of QoE parameters for 5G. This study defines 15 QoE parameters reflecting the aspects of infrastructure and services together with their impact on users. The users' answers are classified into six clusters, which are translated into the innovation enhancement pattern. The findings reveal a pattern in which the Indonesian market is predominantly characterized by satisfied users who expect 5G to provide a better experience than 4G, 3G, and 2G despite the presence of some disappointed users in the market who require specialized innovation with variation in disruptive ways. In terms of 5G infrastructure, certain essential patterns include innovation enhancement to serve remote areas, the increasing significance of connectivity to non-cellular and free-service applications, and the market's constant requirement for a better handset while the local industry may harness the opportunity to complete the 5G ecosystem. In terms of service, essential patterns include a strong need for 5G service innovation to render users more innovative, maintaining societal values while delivering the service, and the service innovation required to connect religious life and the 5G service, which should make users feel more healthy and fit. Finally, we discuss how the patterns recognized herein can help mobile operators to develop appropriate strategies for successfully implementing 5G technology.

In this paper, we propose a new approach for video quality control for multimedia networks. Our new approach is based on video quality measure that combines both the network quality of service (QoS) as well as the user quality of experience (QoE). The proposed approach improves the end-to-end traditional video quality control for multimedia network by including the human perception of video data, which is major concern for the video client, along with the network quality of service (QoS) measurements. We will show that the proposed QoS-QoE based video quality control algorithm can reflect both the condition of the network environment and the human perception of the received networked video data stream. According to both QoS and QoE parameters, rather than using only QoS parameter, video quality control action will satisfy the user needs more than relying only on the network conditions. Since our proposed QoE parameter Self-Reference Complex Wavelet Video Structural Similarity Index (SRCW-VSSIM) can be obtained with no reference (NF) video data, it satisfies the requirement of real-time video transmission.

Evaluating (audio)visual quality and Quality of Experience (QoE) from the user’s perspective, has become a key element in optimizing users’ experiences and their quality. Traditionally, the focus lies on how multi-level quality features are perceived by a human user. The interest has however gradually expanded towards human cognitive, affective and behavioral processes that may impact on, be an element of, or be influenced by QoE, and which have been underinvestigated so far. In addition, there is a major discrepancy between the new, broadly supported and more holistic conceptualization of QoE proposed by Le Callet et al. (2012) and traditional, standardized QoE assessment. This paper explores ways to tackle this discrepancy by means of a multi-instrumental approach. More concretely, it presents results from a lab study on video quality (N=27), aimed at going beyond the dominant QoE assessment paradigm and at exploring affective aspects in relation to QoE and in relation to perceived overall quality. Four types of data were collected: ‘traditional’ QoE self-report measures were complemented with ‘alternative’, emotional state- and user engagement-related self-report measures to evaluate QoE. In addition, we collected EEG (physiological) data, gazetracking data and facial expressions (behavioral) data. The video samples used in test were longer in duration than is common in standard tests allowing us to study e.g. more realistic experience and deeper user engagement. Our findings support the claim that the traditional QoE measures need to be reconsidered and extended with additional, affective staterelated measures.

Recent advances in Wireless Mesh Networks (WMNs) have overcome the drawbacks of traditional wired and ad-hoc networks and now they are seen as a means of allowing last mile communications with quality level assurance in Future Multimedia Systems. However, new routing schemes are needed to provide end-to-end Quality of Service (QoS) and Quality of Experience (QoE) support for delay/loss/jitter-sensitive multimedia applications. The well-known OLSR (Optimized Link State Routing) protocol with ETX (Expected Transmission Count) metric brings many benefits to the path selection process, but has a drawback with regard to queue availability management, which reduces the system performance. This problem is caused when OLSR-EXT control messages are exchanged and the queues of mesh routers along the end-to-end communication path are overloaded. As a result, multimedia-related packets will suffer from loss/delay/jitter and the overall system performance will decrease. This paper proposes the Optimized Link State Routing-Fuzzy ETX Queue (OLSR-FEQ) protocol to overcome the limitations of OLSR-ETX regarding queue availability, QoS and QoE assurance. OLSR-FEQ optimizes network and user-based parameters by coordinating queue availability, QoS and fuzzy issues in the routing decision process as a way of allocating the best paths for multimedia applications. Performance evaluations were carried out with the Network Simulator (NS-2.34) to show the benefits of the proposed solution when compared with existing routing schemes, namely OLSR-ETX, OLSR-FLC, OLSR-MD and HWMP (IEEE 802.11s standard), regarding QoS (unsuccessful packet delivery and throughput) and QoE (PSNR, SSIM, VQM and MOS) parameters.

This paper presents a novel web-based crowdsourcing platform for the assessment of the subjective and objective quality of experience (QoE) of the video service in the cloud-server environment. The user has the option to enter subjective QoE data for video service by filling out a web questionnaire. The objective QoE data of the cloud-server, network condition, and the user device is automatically captured by the crowdsourcing platform. Our proposed system collects both objective and subjective QoE simultaneously in real-time. The paper presents the key technologies used in the development of the platform and describes the functional requirements and design ideas of the system in detail. The system collects real-time comprehensive data to enhance the quality of the user experience to provide a valuable reference. The system is tested in a real-time environment and the test results are given in terms of the system performance. The crowdsourcing platform has new features of real-time network monitoring, the client device, and cloud monitoring, which currently has not been provided by existing web platforms and crowdsourcing frameworks. The results show that 1MB buffer is filled 100% very soon after starting watching videos from the crowdsourcing platform.

The growing demand on video streaming services is the main motive power for video quality assessment. Due to the rapid development of video streaming services, customers are more enjoying higher quality videos, and their perceptions on video service will directly influence the service provider's performance, which makes it significant to study end-users' subjective perception, named as Quality of Experience (QoE), on video streaming for both service providers and end-users. In this paper, a QoE evaluation model is proposed to predict the end-users' perception on video streaming service considering different video content types. This QoE model, named as Video-Mean Opinion Score (VMOS) model, directly focuses on end-users' feeling. Thus, the Key Performance Indicators (KPIs), which can be straightly felt by end-users, are mapped to QoE score without considering the network parameters. The excellent performance of VMOS model for QoE evaluation on video quality has been verified via plenty subjective Mean Opinion Score (MOS) test, which includes 180 video samples with 1280 valid votes. The Pearson Correlation Coefficient between VMOS score and MOS is as high as 0.925, which indicates that this model can evaluate users' perception on video quality with almost the same accuracy as subjective test.

With the development of streaming media service, Quality of Experience (QoE) has become the key competency for those content providers to attract customers. Many researches on QoE assessment have been conducted, but most of them had problems that those subjective experiments are difficult to be regulated and the data is not collected uniformly because of long tail effect. The purpose of this paper is to give a review of QoE assessment, which includes the concept of QoE, the influential factors, the approaches of experiment and QoE analysis and evaluation. Through the survey which shows the development of the QoE assessment, this paper indicates the future trends and challenges. Based on such current challenges and future analysis, an experiment-oriented methodology of QoE assessment is raised by conceptual model description. Also, those technologies which are based on different subjects including communication study and psychology study for supporting the methodology are described. The problems of small data volume, long tail phenomenon in QoE experiments and influence of malicious feedback will be solved by implementation of the personal QoE assessment methodology.

Quality of experience (QoE) serves as a direct evaluation of users' experience in mobile video transmission and is critical to ensure good network service. Although many efforts have been made to predict QoE based on network parameters of the user terminal equipment, it is difficult to predict QoE based on Quality of Service (QoS) offered by the network servers. In this paper, a machine learning based QoE evaluation method is proposed to evaluate user QoE in real-time by analyzing the QoS characteristics for mobile video transmission. For this purpose, we construct a large-scale dataset by collecting more than 300 thousand pieces of metrics data with two kinds of key quality indicators (KQIs) describing the QoE and 91 key performance indicators (KPIs) describing the QoS. A two-process feature subset selection (FSS) method consisting of single parameter pre-FSS and multi-parameter FSS is then proposed to find the KPIs related to KQIs. An Extra-Trees model is finally developed to learn the relationships between the KPIs and KQIs. By employing machine learning and data analytics on network data with the data-driven framework, the proposed method can predict the user QoE according to the QoS of network servers. The results prove that our proposed method can outperform other state-of-the-art approaches.

Multipath diversity leads to a possible higher performance for real-time high definition video, especially for medical video transmission, which would improve the stability of multiple transmission paths in the symmetrical state, and avoid the potential losses of imaging information in the communication process. Most of the previous works are always based on the single-path end-to-end transmission, although the service had been demonstrated that it is unable to meet the rigorous demand for the RT-HDMV. In the paper, a multipath relay service based on the QoE (quality of experience) evaluation method is proposed for the RT-HDMV (real-time high definition medical video). The method eliminates several of the limitations in the existing methods for some conventional single-path transmission. It can fully utilize the finite network resources and transmission bandwidth to meet the users’ demands of the RT-HDMV to get a better score of the QoE. We use a four-stage framework to evaluate the QoE, which consists of constructing the multipath relay transmission for the RT-HDMV, calculating the weights of diversified QoS parameters in the multipath, designing the load distribution strategy by the mapping between the QoS (quality of service) and QoE, and redefining the rule of the QoE evaluation. Many experiments show that the proposed design scheme achieves weighting of the transmission sub-paths and computes the QoE score. Compared with the state-of-art methods in the single path transmission scene, our framework mainly gains the excellent performance for the RT-HDMV.

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Recently, a couple of analytical models for video streaming have been proposed, allowing to compare these input parameters and derive their impact on QoE-relevant metrics for HAS-based video delivery. The outcome of these models are typically asymptotic probabilities, distribution functions, or centralized and standardized moments. For instance, these models do not yield any temporal information in terms of stalling events or requested video quality. This contradicts to QoE prediction models like P.1203, which compute the QoE based on the chronological sequence of a specific video playback. So far, it is unclear how and to which extent the generalized results of analytical models can be utilized to derive sequence-based QoE values or the QoE distribution for a set of sequences for similar input parameters with stochastic variations. To address this problem, we compare testbed measurements with the output of a GI/GI/1 model with pq-policy and buffer-based quality switching capability to conclude to which extent the results still allow to approximate the video QoE.

With the popularity of mobile edge computing (MEC), video streaming's peer-offloading strategy significantly affects the Quality of Experience (QoE) performance of video streaming for mobile users (MUs). Improving the service quality regarding the MUs' demand has become a vital challenge, reflected by QoE. This paper proposes a QoE-aware MEC-based peer-offloading method for HAS-based video streaming, called QOMECS. The proposed method considers dynamic MUs' demands and corresponding QoE requirements. We categorize the QoE KPIs into perceptual and systemic sectors. We formulate the corresponding transmission, computation, and offloading for MEC-based HAS into a QoE maximization problem. We propose a reverse-fuzzied particle swarm optimization (R-FPSO), to solve the highly nonlinear and perceptual-oriented optimization formulation. Unlike conventional fuzzy logic, R-FPSO reverses the fuzzification process by fuzzifying PSO's output (i.e. translated QoE KPIs into satisfactory levels) and further updates the particle values and velocities in the PSO process. Simulation results show that the proposed QOMECS dramatically improves the edge computing efficiency, with optimized QoE performance.

A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video

Augmented reality (AR) can support a user in performing an expert task by overlaying real world objects with the domain specific information required to complete the task. Understanding how users can process and use such information is very important for informing the design of AR technologies and applications. In this paper, the results of a quality of experience (QoE) evaluation of an AR application for the task of solving a Rubik's Cube are presented. The Rubik's Cube was selected based on its familiarity and the expertise needed to solve it unaided. An empirical approach was taken to identify the QoE features that affect the usability and utility of an AR head-mounted display (HMD) compared with paper-based instruction. The QoE evaluation methodology involved the capture and analysis of implicit and explicit QoE metrics. The utility (in terms of performance) of each mode of instruction was objectively measured using: (a) cube completion success rates; and (b) time-to-completion. The implicit metrics of electrodermal activity (EDA), skin temperature, heart rate and the novel use of facial action units (AUs) were recorded to infer emotional state during the task completion. Finally, with respect to explicit metrics, the test subjects completed a Likert scale questionnaire post the experience to subjectively report QoE as well as a self-assessment manikin (SAM) questionnaire to self-report emotional state upon task completion. The results show that AR yielded higher success rates and significantly lower time-to-completion rates. The AR group explicitly reported higher levels of positive valance (affective state) than the paper-based group. The physiological data showed that the AR group were less stressed (via EDA) than the paper-based group. Finally, analysis of the AU data reflected a greater than chance (total: 21.85%) accuracy when predicting affective state based on SAM questionnaires as ground-truth.