Terms Of Mean Opinion Score Research Articles

QoE-Aware Network Pricing, Power Allocation, and Admission Control

In wireless communications, focusing on end-user satisfaction and maximizing network operators' revenue are emerging business challenges. In this paper, we investigate the price-based power allocation problem where, first, base stations (BSs) set prices by maximizing users' utility modeled by their mean opinion score (MOS). Then, each user's optimal power is set by maximizing operator revenue while ensuring the minimum data rate for each user. We propose a hybrid MOS-based pricing method to model users' utility instead of the conventional achievable rate approach. Our hybrid approach applies a machine learning algorithm to model the MOS of the call service in terms of the received signal strength indicator (RSSI). In terms of MOS and outage probability, our proposed method outperforms the rate-based pricing method and the conventional objective MOS models. In addition, we consider a joint admission control and price-based power allocation problem. When a new user requests to connect, a central controller determines whether or not to accept the new connection based on the system's MOS and average outage probability. The results demonstrate a trade-off between the number of users admitted and their level of satisfaction, providing operators with crucial knowledge about how to use their network resources more effectively.

Quality of Experience (QoE)‐based joint admission control and power allocation with guaranteed data rate

AbstractService providers use objective models to map system quality of service (QoS) conditions to an estimated mean opinion score (MOS) in order to assess users' quality of experience (QoE). In contrast with earlier studies, we propose a hybrid model for call services that models the MOS in terms of the received signal strength indicator (RSSI) using a machine learning approach. Unlike most existing studies, which focus on maximizing the sum‐MOS of all users, we aim to maximize the average number of satisfied users in order to allocate optimal power to each user while ensuring the minimum data rate for each of them. Simulation results show that the proposed hybrid model outperforms the conventional objective model in terms of MOS per user and the probability of user satisfaction. Furthermore, when compared to conventional sum‐MOS and sum‐rate maximization problems, users are more satisfied with the proposed problem. In addition, we will present a joint power allocation and admission control problem due to the limited power available to meet the needs of all users. The findings show a trade‐off between the number of admitted users and their level of satisfaction, giving operators valuable insight into how to better utilize their network resources.

Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense environment called heterogeneous networks (HetNet). The optimization problem which arises while maximizing the Mean Opinion Score (MOS) is analyzed along with the QoE(Quality of Experience) metric by considering the Base Station(BS) powers in addition to the needed Quality of Service (QoS). Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature. Hence a deep reinforcement learning framework is developed for tackling the same optimization problem. In this work, a Fuzzy-based Deep Convolutional Neural Network (FDCNN) is proposed in addition to a Deep Reinforcing Learning Framework (DRLF) for extracting the features of highly correlated data. The investigational results prove that the proposed method yields the highest satisfaction to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations. The proposed work outperforms in terms of MOS with multiple antennas.

Estimating Video on Demand QoE From Network QoS Through ICMP Probes

With the increasing traffic of Video on Demand (VoD), network providers are seeking to deliver high Quality of Experience (QoE) for their users. Many methods have been proposed to assess VoD-related QoE. Some of them rely on client instrumentation and reporting QoE information to network elements, such as Server and Network Assisted DASH, others are based on statistical methods that make QoE inferences using monitored network conditions, such as throughput and delays. In this article, we present a practical method to estimate QoE for VoD using the widely supported Internet Control Message Protocol (ICMP) probes. Measured network conditions are used as input to a Machine Learning (ML) model that estimates QoE in terms of Mean Opinion Score (MOS), based on the ITU-T P.1203 Recommendation. The estimation encompasses video quality switches and playback stalls. We estimate MOS with an average Root Mean Square Error (RMSE) of 1.05 for a catalog of 25 different videos, training a model with sessions of the shortest video, and evaluating the generalization to the full catalog. We performed experiments using a virtualized setup as well as in a Wide Area Network.

Mimicking Individual Media Quality Perception with Neural Network based Artificial Observers

The media quality assessment research community has traditionally been focusing on developing objective algorithms to predict the result of a typical subjective experiment in terms of Mean Opinion Score (MOS) value. However, the MOS, being a single value, is insufficient to model the complexity and diversity of human opinions encountered in an actual subjective experiment. In this work we propose a complementary approach for objective media quality assessment that attempts to more closely model what happens in a subjective experiment in terms of single observers and, at the same time, we perform a qualitative analysis of the proposed approach while highlighting its suitability. More precisely, we propose to model, using neural networks (NNs) , the way single observers perceive media quality. Once trained, these NNs, one for each observer, are expected to mimic the corresponding observer in terms of quality perception. Then, similarly to a subjective experiment, such NNs can be used to simulate the users’ single opinions, which can be later aggregated by means of different statistical indicators such as average, standard deviation, quantiles, etc. Unlike previous approaches that consider subjective experiments as a black box providing reliable ground truth data for training, the proposed approach is able to consider human factors by analyzing and weighting individual observers. Such a model may therefore implicitly account for users’ expectations and tendencies, that have been shown in many studies to significantly correlate with visual quality perception. Furthermore, our proposal also introduces and investigates an index measuring how much inconsistency there would be if an observer was asked to rate many times the same stimulus. Simulation experiments conducted on several datasets demonstrate that the proposed approach can be effectively implemented in practice and thus yielding a more complete objective assessment of end users’ quality of experience.

Cache-Enabling UAV Communications: Network Deployment and Resource Allocation

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

Robust Speech Filter and Voice Encoder Parameter Estimation Using the Phase–Phase Correlator

In recent years, linear prediction voice encoders have become very efficient in terms of computing execution time and channel bandwidth usage while providing, in the absence of impulsive noise, natural sounding synthetic speech signals. This good performance has been achieved via the use of a maximum likelihood parameter estimation of an auto-regressive model of order ten that best fits the speech signal under the assumption that the signal and the noise are Gaussian stochastic processes. However, this method breaks down in the presence of impulse noise, which is common in practice, resulting in harsh or non-intelligible audio signals. In this paper, we propose a robust estimator of correlation, the Phase-Phase correlator that is able to cope with impulsive noise. Utilizing this correlator, we develop a Robust Mixed Excitation Linear Prediction encoder that provides improved audio quality for voiced, unvoiced, and transition speech segments. This is achieved by applying a statistical test to robust Mahalanobis distances for identifying the outliers in the corrupted speech signal, which are then replaced with filtered signals. Simulation results reveal that the proposed estimator of correlator outperforms in variance, bias, and breakdown point compared to three other robust approaches based on the arcsin law, the polarity coincidence correlator, and the median-of-ratio estimator without sacrificing the encoder bandwidth efficiency and the compression gain while remaining compatible with real-time applications. Furthermore, in the presence of impulsive noise, the proposed speech encoder speech subjective quality outperforms the state-of-the-art in terms of mean opinion score.

Adaptive QoE-based architecture on cloud mobile media for live streaming

Nowadays, more than 75% of Internet traffic is multimedia traffic, moreover mobile traffic is growing at a rate of 50% each year. All these data together with the evolution of the cloud infrastructures lead us to develop Cloud Mobile Media (CMM) architectures to support the needs demanded by end users. Nevertheless, due to an inherit higher and variable end to end delay mainly as a result of the virtualization process, new challenges appear in particular for live video streaming applications in order to keep a good Quality of Experience (QoE) of the delivered video. Thus, to keep client’s satisfaction within good levels in terms of Mean Opinion Score (MOS), we propose an adaptive QoE-based architecture running on CMM infrastructures for live streaming services. In order to carry out this goal, we propose an estimation of MOS values using an statistical method based on factor analysis. This estimation is based on different measured variables throughout the CMM infrastructure. In addition, we compare the accuracy of the estimated MOS against well-known publicly available video quality algorithms. With these estimations, our proposal is based on two added controllers to the CMM infrastructure: (a) the Software Defined Network controller that acts as a master and (b) the Media Streamer controller. Each one does different actions on the CMM infrastructure in order to maintain and improve the QoE at each end user. Finally, this architecture has been implemented over a fat tree topology in order to show their functionality. The results show that our proposal works properly and it adapts quickly to the network changes in order to deliver a good MOS.

Performance evaluation of different techniques to estimate subjective quality in live video streaming applications over LTE-Advance mobile networks

Current mobile service providers are offering Gigabit Internet access over LTE-Advanced networks. Traditional services, such as live video streaming, over wired networks are feasible on these networks. However different aspects should be taken into account due to the fast changing network conditions as well as the constrained resources of the mobile phones, in order to provide a good subjective video quality in terms of Mean Opinion Score (MOS). Our goal is to estimate and predict this subjective metric without information or reference from the original video, known as Non Reference approach. This approach is important for the Service Provider from a practical point of view, because it can keep the customer satisfaction at good levels. We analyze different estimation techniques running over a set of monitored variables throughout the whole steaming system, from the streaming server to the mobile phone. We have gathered variables related to bit stream, basic video quality metrics as well as Quality of Services variables. These variables are used to estimate MOS in a reliable and robust way. We compare three techniques such as Artificial Neural Networks (ANN), Factor Analysis (FA) and Multinomial Linear Regression, at different time scales and with Full Reference and Non Reference approaches. We carry out a performance evaluation of these techniques, concluding that the behavior of MOS estimation based on FA is more accurate, unless we had a lossless scenario related to Guaranteed Bit Rate services, where ANN performs better. The subjective video quality has been evaluated through surveys. Finally, we evaluate the accuracy of the estimated MOS against well known publicly available video quality algorithms following the recommendations given by Video Quality Experts Group (VQEG).

QoE-Oriented Rate Allocation for Multipath High-Definition Video Streaming Over Heterogeneous Wireless Access Networks

Global mobile video traffic is predicted to grow explosively in the near future and high-definition (HD) video streaming applications are increasingly prevailing over the mobile Internet. To optimize the transmission performance of HD video streaming applications over the heterogeneous wireless access networks, multipath video streaming that exploits the multihoming features of mobile devices is proposed as a potential solution. However, the delivery of HD video has much higher bitrate and more stringent delay constraint than other services. It is thus challenging to transfer HD video through wireless networks. Conventionally, multipath video streaming is optimized by the video distortions or other network-level quality-of-service parameters, which do not correlate well with the human visual perceptions. To address this problem, we first propose a novel quality-of-experience (QoE) prediction model to prefer the QoE values of different rate allocation scenarios over heterogeneous wireless access networks. Then, the rate allocation problem over heterogeneous wireless networks is formulated as a nonlinear optimization problem of maximizing the perceived video quality at the receiver. A relaxing function called penalty function is exploited to transform the rate allocation problem into an unconstrained optimizing problem and the pattern search method is adopted to obtain the close-to-optimal solution in each rate allocation session. The extensive simulation results show that our proposal significantly improves the QoE in terms of mean opinion score compared with the reference schemes.

Non‐parallel training for voice conversion using background‐based alignment of GMMs and INCA algorithm

Most of the voice conversion (VC) researches have used parallel training corpora to train the conversion function. However, in practice it is not always possible to gather parallel corpora, so the need for non-parallel training methods arises. As a successful non-parallel method, nearest neighbour search step and a conversion step alignment method (INCA) algorithm has attracted a lot of attention in recent years. In this study, the authors propose a new method of non-parallel VC which is based on the INCA algorithm. The authors' method effectively solves the initialisation problem of INCA algorithm. Their proposed initialisation for INCA is done with alignment of Gaussian mixture models (GMM) using universal background model. Results of objective and subjective experiments determined that the authors' proposed method improves the INCA algorithm. It is observed that this superiority holds for different sizes of training material from 10 to 50 training sentences. In terms of mean opinion score, the authors' method scores 0.25 higher in the case of quality and 0.2 higher in the case of similarity to the target speaker compared with traditional INCA. It seems that the authors' proposed method is a suitable frame alignment method for non-parallel corpora in VC task.

Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation.

Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions using the extracted features, which are not optimized with respect to human opinion. To cope with this limitation of the conventional approach, we introduce a novel deep learning scheme for NR S3D IQA in terms of local to global feature aggregation. A deep convolutional neural network (CNN) model is trained in a supervised manner through two-step regression. First, to overcome the lack of training data, local patch-based CNNs are modeled, and the FR S3D IQA metric is used to approximate a reference ground-truth for training the CNNs. The automatically extracted local abstractions are aggregated into global features by inserting an aggregation layer in the deep structure. The locally trained model parameters are then updated iteratively using supervised global labeling, i.e., subjective mean opinion score (MOS). In particular, the proposed deep NR S3D image quality evaluator does not estimate the depth from a pair of S3D images. The S3D image quality scores predicted by the proposed method represent a significant improvement over those of previous NR S3D IQA algorithms. Indeed, the accuracy of the proposed method is competitive with FR S3D IQA metrics, having ~ 91% correlation in terms of MOS.

YouTube QoE evaluation tool for Android wireless terminals

In this paper, we present an Android application which is able to evaluate and analyze the perceived quality of experience (QoE) for YouTube service in wireless terminals. To achieve this goal, the application carries out measurements of objective quality of service (QoS) parameters, which are then mapped onto subjective QoE (in terms of mean opinion score, MOS) by means of a utility function. Our application also informs the user about potential causes that lead to a low MOS as well as provides some hints to improve it. After each YouTube session, the users may optionally qualify the session through an online opinion survey. This information has been used in a pilot experience to correlate the theoretical QoE model with real user feedback. Results from such an experience have shown that the theoretical model (taken from the literature) provides slightly more pessimistic results compared to user feedback. Users seem to be more indulgent with wireless connections, increasing the MOS from the opinion survey in about 20% compared to the theoretical model, which was obtained from wired scenarios.

A novel scheduling approach to concurrent multipath transmission of high definition video in overlay networks

Recent advancements in network infrastructures provide increased opportunities to support video delivery over multiple communication paths. However, the high definition (HD) video transmissions still pose crucial challenges due to the high throughput demands and large-size video frames. Motivated by optimizing the delay performance for concurrent multipath transmission of HD video, we propose a novel scheduling approach dubbed FSWG (Frame Splitting based on Weibull distribution and Graph theory) that aims to minimize the end-to-end frame delay while alleviating out-of-order arrivals. First, we analytically construct a delay performance model for HD video streaming in multipath overlay networks based on Weibull distribution and graph theory. Second, we formulate the frame splitting over parallel paths as a constrained optimization problem of minimizing total frame delay and derive its solution based on the water filling algorithm. Third, we design a multipath video transmission system to implement the proposed scheduling approach. The performance evaluation is conducted through extensive simulations in QualNet using H.264 video streaming. Experimental results show that FSWG outperforms the existing schemes in terms of Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), and delay performance metrics.

Deconvolution-based acoustic source localization and separation algorithms

In this paper, localization and separation of acoustic sources are examined. Depending on the number of sources in relation to the array channels, the problem is investigated in terms of underdetermined and overdetermined configurations. In the underdetermined configuration, virtual monopole sources are assumed in uniformly spaced angles. The problem is then formulated into compressive sampling (CS) problem which can be solved by using the linearly constrained -norm convex (CVX) optimization. The solution yields the directions of real sources and the source signal spectrum, which enables localization and reconstruction of sources at one shot. In the underdetermined configuration, source localization and signal separation is carried out in two steps. First, the directions of arrival (DOA) are estimated with Minimum Variance Distortionless Response (MVDR) or Multiple Signal Classification (MUSIC). Next, Tikhonov regularization (TIKR) is utilized to recover the source spectrum. In the localization problem for both configurations, Neyman-Pearson detector is employed to determine thresholds for source detection. Numerical and experimental results show that the proposed methods produce improved speech quality in terms of mean opinion score (MOS) in perceptual evaluation of speech quality (PESQ) test.

Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client

In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11–18% in terms of mean opinion score in a wide range of network configurations.

An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing

A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter-bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks.

Pragmatic Correlations of Quality-of-Experience and Quality-of-Service in IMS-Based IPTV Network

To let the service system react quickly on customers’ perception or user experience while using the service, mapping functions between Quality of Experience (QoE) and Quality of Service (QoS) are strongly required in purpose of building up an intelligent QoE control system upon adjusting QoS parameters. This article studies the changing behavior of QoE with respect to changes of QoS parameters in the context of video streaming service in an IP Multimedia Subsystem-based IP Television network (IMS-based IPTV network). The article is, in fact, an extended version of the paper published by the same authors (Thu-Huong Truong, 2012). In (Thu-Huong Truong, 2012), the authors studied QoE in both terms of Mean Opinion Scores and VQM as functions of each single QoS parameter such as: loss, jitter, and delay. In this extended content, the correlation between QoE and multiple QoS parameters will be introduced. The QoE-QoS correlation could be a significant first step to build a smart QoE monitoring and control mechanism as an added value to promote the IMS-based IPTV network.

Substitution of G.728 vocoder’s codebook search module with SOM array trained by PSO-optimized supervised algorithm

Low delay-code excited linear prediction (LD-CELP) is an attractive algorithm in implementing vocoders in voice over Internet protocol networks. This algorithm has been proposed for the coding of speech at 16 kbps with toll quality. However, operation at transmission rates lower than 16 kbps is desirable, so that traffic can be accommodated during system overload conditions. In this paper, an array of self-organizing maps (SOMs) is employed instead of traditional codebook search module, recommended in ITU-T G.728, to determine the optimum index value of shape codebook. It is noted that a modified supervised training algorithm is used for SOMs in which some of the training parameters are optimized using particle swarm optimization (PSO) algorithm. Based on the occurrence frequency characteristics of codevectors, six bits for shape codebook and two bits for gain codebook are used in this work to produce a vocoder with lower bit rate as compared with traditional ITU-T G.728 vocoder. The performance comparison of the proposed SOM array trained by PSO-optimized supervised algorithm as the codebook search module in the structure of LD-CELP with a conventional implementation of LD-CELP coder shows that execution time of the algorithm is reduced up to 44 %. However, the degradation of voice quality in terms of mean opinion score, perceived evaluation of speech quality and segmental signal-to-noise ratio (SNRseg) is acceptable.

TEXT-INDEPENDENT VOICE CONVERSION BASED ON CHINESE PHONEME CLASSIFICATION AND KERNEL EIGENVOICES GAUSSIAN MIXTURE MODEL

This paper proposed a novel algorithm for text-independent voice conversion based on Chinese phoneme classification and kernel eigenvoices Gaussian mixture model. The phoneme classification can avoid the disturbance of linguistic information and spectral smoothing. A speaker adaptation technique of kernel eigenvoices was employed for performing spectral conversion between speakers for each category phoneme, adapting the conversion parameters derived for the pre-stored pairs of speakers to a desired pair, which can relax the parallel constraint effectively. Objective test on the spectral conversion accuracy demonstrated that the proposed kernel algorithm can effectively exploit the nonlinearity in supervector space. In subjective listening test, an ABX test was performed and the proposed algorithm was preferred to the existing eigenvoice algorithm by 4.75%, and improved quality by 10.91% in terms of mean opinion score (MOS). Both objective and subjective tests demonstrated that the proposed algorithm effectively enhanced speech quality and speaker individuality in a text-independent manner.