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This paper examines influences of the network latency on cooperative work in a networked virtual environment with haptics by QoE (Quality of Experience) subjective and objective assessment. As the work, two users cooperatively carry an object in a networked maze system by using haptic interface devices. Assessment results demonstrate that as the network latency becomes longer, MOS (Mean Opinion Score) as a subjective measure becomes higher. We also clarify the relations between MOS and several objective measures by regression analysis.

In literatures, many works on the area of massive MIMO (Multi Input Multi Output) presented the improvement of signal quality by using precoding in accordance with partial structure. However, only signal quality is not good enough to represent the satisfaction of users. In this paper, we proposed the precoding for partially-structured massive MIMO by utilizing user satisfaction in term of Quality of Experience (QoE). The level of Mean Opinion Score (MOS) is adopted to express QoE of web browsing service. In the first step, we optimize the baseband precoding and RF precoding by proposing the alternating minimization methods, then optimize the QoS parameters for the best QoE. The results show that the optimized QoS parameters lead to the satisfaction of user and the achieving of the required level of QoE.

With the development of converged optical-wireless networks, the mobile users can access a very high bandwidth, any time and anywhere, for the upcoming real-time applications. In the modern digital world, quality-of-experience (QoE) has become the central area of concern to ensure end-to-end user satisfaction. In this paper, we analyze the quality-of-service (QoS) for different traffic classes for a radio-over-fiber (RoF) based attocell network. For this, we use a simple polling algorithm with dynamic weighted queuing for grant sizing and maintains a delay within the maximum bounds for high and medium priority traffic classes with high channel utilization. Further, we propose a model to map QoE with the QoS parameters, like delay, jitter, and packet loss rate, and quantify QoE by representing the degree of user satisfaction by a mean opinion score.

The rise in popularity of the streaming platforms for gaming videos like Twitch.tv results in more users watching and streaming gaming videos passively. These streaming videos show great dependence on Quality of Service (QoS) parameters like latency and packet loss. This motivates us to evaluate the Quality of Experience (QoE) of three popular streaming game videos for different Packet Loss Ratios (PLR). In this paper, using streaming gaming videos, we demonstrated the limitations of using Mean Opinion Score (MOS) to evaluate QoE. Using a novel method of data representation, we presented our results as distributions of user-rating at different PLR configuration and compared them with other subjective QoE evaluation methods like MOS and standard deviation of Opinion score (SOS). Our results show that evaluating QoE using MOS loses information about user diversity. Our findings also include SOS retaining user diversity but posing challenges to quantifying the QoE at varying PLR values. For streaming video games we show that QoE distributions evaluate user perception accurately. Our work in this paper also studies how different games with different content complexity, genre and pace are affected by PLR. Our results show that slow-paced games like FIFA 20 get affected more significantly by packet loss.

Designing a resource allocation technique for a cognitive radio network (CRN) adopting underlay dynamic spectrum access (DSA), requires a cross-layer approach for an acceptable quality of experience (QoE) in view of having differentiated quality-of-service (QoS) classes of traffic. This paper focuses on heterogeneous traffic management in underlay CRNs, where the transmission parameters are optimized to not only satisfy the interference constraint of the primary link but meet the delay requirement of the video traffic. The paper develops a deep reinforcement learning-based resource allocation technique which significantly lowers the queuing delay at the data link layer and maximizes the network average end-to-end mean opinion score (MOS). Simulation results show the effectiveness of the proposed cross-layer technique over a single-layer approach. The cross-layer technique effectively changes the transmission parameters in accordance with the delay requirement of the video traffic and the underlay DSA interference constraint without compromising the MOS.

The reliable delivery of packets is the major concern for the entire Internet service providers (ISPs) using Voice over internet protocols (VoIP). Therefore, quality of service (QoS) is a fundamental task for all VoIP using organizations. Unfortunately, there is no guaranteed way to ensure QoS, due to the fact that the QoS labels may be peeled off the packet while in the transition through various network elements. The performance of a network can be enhanced by Integrating QoS with internet protocol (IP) and multiprotocol label switching (MPLS) networks. In this paper we analyzed the QoS performance of VoIP in converged MPLS networks. Since IP systems are exceptionally dynamic, therefore progressive traffic monitoring and administration are needed before VoIP is even transmitted in a network infrastructure. This study provides a new configuration administration over an ISP network infrastructure comprised of converged networks carrying multiple VoIP instances. It is achieved using route reflectors and route redistribution at micro level to ensure a guaranteed threshold QoS at acceptance level in adverse circumstances.

Adaptive quality management schemes in Voice over IP (VoIP) Network have a constructive impact on the improvement of voice quality level received by end-users. These schemes are used for persistent monitoring of quality level leading to better management of network services. In this paper, a model is introduced to improve the voice quality level and optimize resource utilization. In the proposed model, the VoIP network makes the decision for configuration adaptively and modifies itself by means of Hidden Markov Model (HMM). Depending on the quality level and consumed bandwidth evaluated in the receiver side this adaptive self-modification takes place. During this procedure, acceptable weights for Quality and Quality of Service (QoS) parameters, namely, End-to-End Delay, Packet Loss, and Jitter are determined. These acquired weights help HMM model predict next optimum state properly. Another important objective of this work is to find a relation between Quality of Experience (in terms of Mean Opinion Score) and Quality of Service (in terms of End-to-End Delay and Jitter) parameters. This mapping provides a tangible understanding of how QoS parameters affect MOS. Simulation results demonstrate that the proposed technique lessens the impact of jitter and packet loss on voice quality level.

In recent years, the variety and volume of multimedia services have increased exponentially. Like most of other multimedia services, the conversational video service has stringent quality of service and experience requirements. In order to better support users QoE (Quality of Experience) and allocate network resources more effectively, this paper focuses on the QoS (Quality of Service)-QoE association modeling of conversational video flows. Through statistical analysis of the network video flow data, statistical distribution association between QoE and some network layer QoS parameters of the conversational video service is studied by use of the ITU-T P.1201.1 model, and relevant distribution functions are fitted. From the perspective of user’s experience, the QoS parameter recommendation indices are given for each MOS (Mean Opinion Score) value interval, and a QoE connected QoS model is established. Through the verification and comparison with an existing work, the rationality of the recommended QoS indices is demonstrated, which can help the network operators strike a balance between saving network resources and satisfying user’s quality of experience.

Quality-of-experience (QoE) driven resource allocation will play a huge role in the deployment of cognitive radio networks (CRNs) towards the 5G era. Cognitive approaches need to exhibit a certain degree of intelligence in order to draw optimal decisions when dealing with heterogeneous traffic. However, with current methods such as reinforcement learning (RL), efficient exploration of the state space still remains a major challenge. The epsilon (e)-greedy exploration strategy suffers from poor convergence since it does not carry out deep exploration. This consequently results in more convergence cycles or exponentially large data requirements. The docitive paradigm, which is an extension of the cognitive paradigm, performs well using RL but it becomes unfeasible when the network becomes distributed with a large number of nodes. Thus, it becomes a huge challenge to perform user experience evaluation of multimedia services on-the-fly since this requires the collection and correlation of a mixture of variables on network conditions, the service, as well as the user. As our first step towards addressing this problem, we perform resource allocation using the mean opinion score (MOS) for heterogeneous traffic. We then demonstrate that deep Q-networks (DQNs) can combine deep exploration with deep neural networks (DNNs) for exponentially faster learning and convergence. Our results indicate that docitive approaches have a 55% better convergence rate compared to individual learning at 5 SUs and 70% better at 42 SUs. Also, perfect docition performs 6.67% better than the other approaches when the number of SUs reaches 32.

In this paper a new method to improve the mathematical estimation of Quality of Experience (QoE) perceived by the end user of a Voice call over LTE (VoLTE) is presented. The new estimation leads to Mean Opinion Score (MOS) values more realistic than those available on network monitoring processes. The optimization of the QoE is calculated as a function of several QoS parameters, in particular packet loss, network jitter delay and jitter buffer dimension. The improvement has been realized by identifying the QoE and QoS parameters, named respectively Key Quality Indicators (KQI) and Key Performance Indicators (KPI), for VoLTE services, and then applying a QoE/QoS mathematical relationship in order to adjust the expected QoE level on the basis of QoS.

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users’ Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R2 value) regarding the same parameters using regression.

In mobile networks, there exists a relationship between the quality of service (QoS) and the quality of experience (QoE). This is often determined by the mean opinion score (MOS). There are several approaches that are used to determine the MOS in mobile networks and these may be subjective and objective. While the subjective approaches are standardized in their own essence, they are expensive and laborious to perform. Often, objective approaches have been used to determining the MOS by using network parameters such as delay, jitter and packet loss that are normally derived from network interfaces. The main aim of this study is to reduce the cost of the capital expenditure (CAPEX) and the operational expenditure (OPEX) linked to the drive testing method of obtaining the MOS. Thus, in this paper, a cost-effective objective MOS evaluation method based on the core network parameters from the Nb interface of a 2G and 3G mobile network are used. The training and verification of the proposed model was evaluated using machine learning techniques with regression analysis. The experimental results of the proposed model were compared with the subjective MOS evaluation from the drive test. Generally, the results showed that there was a high correlation between the predicted MOS and the values collected through the drive test, which is a subjective MOS evaluation approach.

A Local Area Network is designed and simulated to investigate the effect of Quality of Service (QoS) on the performance of the network. The experiment examines the performance of non-QoS scenario and QoS scenario. Voice Jitter, Voice Mean Opinion Score value, Voice Packet End-to-End Delay, Voice Packet Delay Variation, Voice traffic received, and Voice traffic sent are used as performance parameters. The result demonstrates that QoS scenario outperformed non-QoS scenario. Therefore, the Quality of Service (QoS) has improved the overall performance of the network.

In Smart Cities, Vehicular Ad-Hoc Networks (VANETs) will cover an important role with a development of safety application and smart vehicle video surveillance services. With an on-board screen and smart components, multimedia communication over VANETs let drivers, passengers to capture live scenes, share, and access on-road multimedia services. In a monitored area, vehicles can transmit live video streaming of disasters or traffic accidents and provide significant visual information. In this research we propose an application offering multiple services in a smart city’s VANET. As, the main concern of the video streaming transmission in such highly dynamic environments is the enhancement of the Quality of Experience (QoE), one of the critical issues in VANET is the design of a proper routing protocol which handle this variety of services. In this paper, we focus on multi-hop level forwarding and we propose a Delay, Quality of Link and Link lifeTime aware routing protocol suitable for Video streaming (DQLTV). In the aim to find out a route from communicating vehicles, we define a mathematical model for simultaneously maximizing the quality of link and link lifetime and minimizing the distance coupled with hop count in order to minimize the delay. To demonstrate the effectiveness of DQLTV, we compare it with the AODV-ETX protocol in terms of QoE and QoS metrics. The simulation results show that our model outperforms AODV-ETX in terms of QoE metrics, the PSNR (Peack Signal to Noise Ratio), MOS (Mean Opinion Score) and SSIM (Structural SIMilarity). Our protocol also outperforms AODV-ETX in terms of QoS metrics, the end-to-end delays and frame loss.

Voice over Internet Protocol (VoIP) is a method of transmitting audio and video over a network. It is a low-cost and readily available alternative to traditional landline phones. In this paper we discuss the foundational protocols of VoIP and analyze the Quality of Service (QoS) of several applications. Our testing consisted of capturing packets during a conversation to determine packet loss, throughput, delay, and jitter. Several listeners subsequently provided Mean Opinion Scores (MOS) of several audio snippets based on phonetic clarity. We determine that Skype provides the highest quality and most consistent service.

The significant advancements achieved in wireless communications over the past few years has facilitated successful deployment of LTE-A, and heralded great efforts in 5G development. However, with the increase in the number and variety of connected devices, wireless video transmission in real-time is challenging for the aforementioned network paradigm. Many studies have shown that the centric focus of communications should be the content type, rather than the communication itself, which means real-time multimedia communications are considered crucial for future Internet architectures. This requires high capacity channels and techniques to mitigate inherent wireless channel errors. Thus, we propose an application-layer and middleware-based solutions that increase network reliability and flexibility and provide Quality of Service (QoS) control based on Scalable Video Coding (SVC). Due to the real-time and QoS support of the Data Distribution Service (DDS) middleware, it can be used to implement the three types of SVC scalability: Viz. Temporal, Spatial, Quality (SNR). The open source Scalable Video-streaming Evaluation Framework (SVEF) tool has been used to assess the video transmission performance with performance metrics, Viz. Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), and frame delay. The results showed a graceful degradation of video quality when using the DDS-based SVC, particularly when the number of receivers is increased. The acquired results show excellent improvements which can be applied to different Future Internet architectures.

In the era of information, the services of fog computing environment with content delivery are expected to offer users the better satisfaction of Quality-of- Experience (QoE) than that in a conventional environment. Nevertheless, the dataflow and new demands from users increase along with the promising of content-centric computing system in fog computing environment. Therefore, the satisfaction of QoE will become the major challenge. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the quality of service in fog computing environment concerning both system and users. The value of QoE does not only refer to the system cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture influential factors from system cost based on system states and services for users. Specially, we mainly focus on issues of cache allocation and transmission rate. Under this fog computing environment, aiming to the capacity of cache allocation among fog nodes and handle transmission rates under a constrained total system cost and MOS, we devote our efforts to the following two aspects. First, we formulate the QoE as a joint resource allocation problem under different transmission rates to acquire best QoE. Then, we propose a dynamic algorithm based on shortest path tree (SPT), which is suitable for fog computing environment with content delivery frequently. Simulation results reveal that the benefit for using the dynamic allocation (DA) method to allocate resource can achieve high QoE performance.

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.

This paper presents a novel Quality of Experience (QoE) prediction model, for voice and web browsing based on real Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) data. It estimates the user perceived quality, in a Mean Opinion Score (MOS) scale, by evaluating several Radio Frequency (RF) channel measurements and Quality of Service (QoS) metrics. Real Drive Test (DT) data and MOS measurements were used as reference data, in order to produce a new QoE prediction model, using machine learning techniques. The Support Vector Regression (SVR) algorithm was used to map the QoS metrics into MOS. The new developed model enables the application of QoE as a more realistic network optimization criteria. The QoE model for voice calls presented a Root Mean Square Error (RMSE) of 11% and a correlation of 62%, when comparing the predicted MOS to the one that was measured. The web browsing model showed an higher correlation (of 92%) and a lower RMSE (of 10%).

Video streaming over MANET (Mobile Ad-hoc Network) is challenging due to specific features that characterize this network. The importance of MANET-specific features has been already emphasized in terms of QoS (Quality of Service). Nevertheless, these parameters have been considered to a limited extent in terms of QoE (Quality of Experience), and thereby, they still remain not well understood. Therefore, this research has been motivated by the challenge to provide a deeper understanding of MANET-specific parameters and their impact on QoE for video streaming. Experimental study has included two phases, i.e., video streaming emulation over MANET and end-user survey to collect MOS (Mean Opinion Score) rates. Results obtained from the statistical analysis imply that there exists strong and statistically significant impact of individual MANET-specific parameters and their interaction on QoE for video streaming.

This paper presents a machine-learning software solution that performs a multi-dimensional prediction of QoE (Quality of Experience) based on network-related SIFs (System Influence Factors) as input data. The proposed solution is verified through experimental study based on video streaming emulation over LTE (Long Term Evolution) which allows the measurement of network-related SIF (i.e., delay, jitter, loss), and subjective assessment of MOS (Mean Opinion Score). Obtained results show good performance of proposed MOS predictor in terms of mean prediction error and thereby can serve as an encouragement to implement such solution in all-IP (Internet Protocol) real environment.

There is a substantial body of literature on analyzing Quality of Experience (QoE) of Video Streaming while there are few studies on standardizing QoE assessments. One of recent proposals on standardizing QoE of video streaming is video Mean Opinion Score (vMOS), which can model QoE of video streaming in 5 discrete grades. However, there are few studies on quantifying vMOS and investigating the relation between vMOS and other Quality of Service (QoS) parameters. In this paper, we address this concern by proposing a data-driven QoE analysis framework on video streaming QoE data. Moreover, we conduct extensive experiments on realistic dataset and verify the effectiveness of our proposed model. Our results show that vMOS is essentially affected by many QoS parameters such as initial buffering latency, stalling ratio and stalling times. Interestingly, we have found that a small set of QoS parameters play an important role in determining vMOS.

This paper proposes: 1) a traffic flow management policy, which allocates and organizes machine type communication (MTC) traffic flows network resources sharing within evolved packet system (EPS); 2) an access element as a wireless sensor network gateway for providing an overlaying access channel between the machine type devices and EPS; and 3) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices, and the related quality of service (QoS) issues among them. This paper overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in packet loss rate (PLR), a decrease of 99.89% in packet end to end (E2E) delay, a decrease of 99.21% in packet delay variation (PDV). Furthermore, it retains the perceived quality of experience of the real time application users within high satisfaction levels, such as the voice over long term evolution service possessing a mean opinion score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardized values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV.

In a wireless ad hoc multimedia network, how does the Quality of Experience (QoE) of the user affect the level of Situational Awareness (SA)? Is maximum QoE necessary for high SA? In this empirical study, we propose a novel measurement approach to quantify SA based on the QoE of the user. The relationship between QoE and network Quality of Service (QoS) metrics such as delay and packet loss is well known. Therefore, quantifying the SA-QoE-QoS relationship will help the network operator to ensure a high level of SA when the network is under attack, through parsimonious allocation of network resources such as spectrum and power. We first define SA metrics in four contexts: Surroundings awareness, Target awareness, Location awareness, and Responsiveness (SETLR model). Next, we design an experiment and define an objective expression for each SETLR metric. We mathematically formulate SA as a function of QoE, using real data, and show that this relationship is logistic in nature. This new utility function for measuring SA can now be used in network optimization techniques to avoid over-provisioning. Finally, we observe that maximum QoE is in fact not necessary to ensure high SA; a Mean Opinion Score of just 2–3 is necessary in our scenario.

Unprecedented bandwidth-intensive, ultra-low latency multi-media services and applications are expected to be delivered through the fifth generation (5G) network. Thus, efficient radio resource management will continue to play an important role as the network service providers strive to provide rich media contents to their users. Therefore, in this work, we consider a cross-layer based framework for efficient allocation of network resources for the transmission of HE VC encoded video streams over 5G-aligned V2X application in mmWave environments. We adopt application and physical layer models, and formulate a multi-vehicle resource allocation problem taking into account the abstracted information of the two corresponding layers. Such a framework is compared with three other standard resource allocation schemes using computed Mean Opinion Score (MOS). Numerical results show that our proposed radio resource management scheme offers a significant improvement of viewer-perceived quality of service compared to the reference approaches.

According to Forbes collected data, the public cloud services market will rise in 2016 and is expected to grow to $105 billions and, in fact, Virtual Machines (VMs) are one of the most popular tools for building “Cloud” services. But in service delivery such composition of multiple components greatly complicate analysis of product quality, especially if it is about Voice over IP (VoIP) set of services, which performance essentially depends on network quality. We provide a solution, that allows to perform deep network packet inspection with quality of service measurements with some quality of experience predictions, like some form of mean opinion score for VoIP. All system consists of a few completely open-source projects, developed with several professional teams. Currently system support Linux and QEMU/KVM-based solutions and is completely stream-based because of impossibility to save such amounts of data on 10Gbit link.

Video has become a new fundamental service of mobile networks. In addition, end-users are always expecting for better service quality. One of the main challenging task in 4G LTE mobile networks is how to satisfy users quality of experience QoE for video applications. A video Mean Opinion Score vMOS is proposed by Huawei to evaluate the QoE of the end-user experience. In this paper we have used vMOS as tool to evaluate video experience of DJEZZY consumers and compared it to subjective tests based on a panel of human. In addition, we have developed an application “Mobile vMOS calculation” to help operator to plan and construct mobile broadband networks for video services.

Context-oriented computing has introduced new challenges in Quality of Service (QoS) management since the diversity of applications, forms of access and the TCP/IP architecture model make it difficult to provide services aware of Quality of Experience (QoE). In view of that, we propose an autonomous architecture of service provision and delivery that is QoE aware. The proposed components were designed and validated in a Software-Defined Networking environment. The results demonstrate that the solution is able to collect and infer the user's experience; map the QoS parameters in QoE to predict the Mean Opinion Score, and act autonomously to adjust the resources in the underlying network infrastructure, avoiding degradation of QoE.

Quality of Experience (QoE) management is a prime topic of research community nowadays as video streaming, online gaming and security applications are completely reliant on the network service quality. Moreover, there are no standard models to map Quality of Service (QoS) into QoE. HTTP media streaming is primarily used for such applications due to its coherence with the Internet and simplified management. The most common video codecs used for video streaming are H.264/AVC and Google's VP8. In this paper, we have analyzed the performance of these two codecs from the perspective of QoE. The most common end-user medium for accessing video content is via home based wireless networks. We have emulated an error-prone wireless network with different scenarios involving packet loss, packet delay and delay variation. The focus of this paper is evaluating the end user satisfaction with the multimedia content by subjective assessment using Mean Opinion Score (MOS). We have observed the overall superiority of H.264/AVC but the VP8 codec strongly gains ground in highly error-prone networks in terms of user satisfaction.

When dealing with Quality of Experience (QoE) and in particular perceptual quality assessment and modeling, averaging is a common occurrence. For instance, the most commonly used measure of QoE is the aptly-called Mean Opinion Score (MOS), which is intended to represent an idealized average subject's rating of the quality. Another form of averaging occurs when choosing and preparing the samples used for the assessment, which are supposed to be representative of an average viewing situation. This leads to nice, smooth scalar representations of quality, but at the same time, it leads to a loss of information. In this paper we present a first step towards working with all the information available in an explicit way, rather than averaging it away. We do so in the context of constructing layered quality models for HTTP video streaming (using Dynamic Adaptive HTTP Streaming — DASH, excluding its adaptation feature at this stage), mapping network-level QoS measurements to probability distributions of different MOS values for a given set of conditions.

The performance and quality of VoIP calls conducted through a Mobile Ad Hoc Network (MANET) having a number of SIP wireless nodes registered on a SIP Proxy are evaluated. The measurement campaign is conducted in an indoor corridor environment at a university campus. The voice codecs used to conduct the calls are G.711, G.727 and G.723.1. These codecs are mainly used in VoIP technologies. The calls' quality is evaluated using four QoS metrics which include the mean opinion score (MOS), jitter, delay, and packet loss. According to the experimental results, voice codec G.711 provides the best performance in this indoor environment.

Quality of service (QoS) for voice over IP network is not consistent because of absence of dedicated links as in circuit switched (CS) network. Because of wider mobility to users in UMTS network and voice over IP network being low cost service, performance evaluation for VoIP service over UMTS network is realized more important. Prioritized forwarding strategy to different traffics can be controlled allocating Forward Access Channel (FACH) Scheduling weights in Radio Network Controller (RNC) and Resource Reservation Protocol (RSVP) for the resource allocation like buffer space, bandwidth etc. In this paper, End-to-End QoS of UMTS is mapped to external IP network with Differentiated Service Code Point (DSCP) field in IP packet header specifying different per hop behavior (PHB) and significant improvement in QoS of VoIP service over UMTS network has been observed. Hybrid model of QoS enhancement with DiffServ Code Point integrated to FACH scheduling weight inside UMTS network and RSVP scenario has been studied in this work and the model has been purposed to reduce End-to-End delay maintaining better Mean Opinion Score (MOS).

We consider Quality of Experience (QoE), measured in terms of Mean Opinion Score Listening Quality Subjective (MOS_LQS) for a Voice over LTE (VoLTE) application in realistic situations. Groups of service scenarios have been identified and the network performance simulated. We organized a listening panel to measure MOS according to standard procedures. MOS-LQS results and QoS metrics have been correlated using a set of Artificial Neural Network (ANN) models. As a clear result the ANN accurately models the relationship. This confirms again the need for an approach that is non linear and capable of generalization, as made by the human judgment. Future research is necessary to generalize the ANN model to a far larger scale while controlling the combinatorial explosion of scenarios and technical parameters.

In the past, the Quality Of Service (QOS) was the key performance of every network because it could be really measured and helped engineers to improve the network services. However, QOS parameters reflect only the network performances which do not directly indicate the satisfaction of users. Some users experience a bad service due to crowd accessibility even though the signal strength is still good. Hence, this paper proposes the estimation model of user satisfaction in terms of Quality Of Experience (QOE) by using neural network approach. The input of this model is obtained from the measured QOS parameters. Firstly, the authors determine the correlation of QOE due to the QOS. The algorithm used for analysis is Artificial Neural Networks (ANN) toolbox in Matlab software. Data Collection QOS parameter and Application Mean Opinion Score (AMOS) in each user of different networks were used to learn in neural networks to get the right weights in QOE correlation model. The role of this research is to demonstrate the use of neural network approach to create QOE model in the assessment of user satisfaction resulted from the QOS parameters instead of directly measuring the Mean Opinion Score (MOS) from users.

Nowadays major interest in the field of Dynamic Telecytology is growing rapidly. The need of the pathologist to analyze the specimens remotely both hardware and software[1][2]. Therefore, authors are trying to do a study to examine the Quality of Service (QoS) feasibility of the Telecytology system using Wireshark, Essential NetTools and Ping Plotter. This research was conducted in Greater Jakarta, Indonesia. There are 2 (two) systems that will be examined for this research. The first system is the Raspberry Pi 2 and WebCam. The second system is the Android smartphone and Universal Microscope Adapter. The purpose of this study is to prove that the Telecytology system can be implemented by displaying images captured by a microscope and displayed at a remote location with an image resolution, frame rate, delay, latency, jitter, throughput and Mean Opinion Score (MOS) are desired and with the expectations as follow : The minimum of the video image resolution is at least 320 × 240 pixels, the best frame rate is 30 fps, the latency is below 400 ms, the packet loss is below 1%. The value of MOS are between 3 and 4, expected to prove that the system is feasible to be applied on Broadband and 3G/4G network in Indonesia.

In this paper, we study the influence of video stalling on QoE. We provide QoE models that are obtained in realistic scenarios on the smartphone, and provide energy-saving approaches for smartphone by leveraging the proposed QoE models in relation to energy. Results show that approximately 5J is saved in a 3 minutes video clip with an acceptable Mean Opinion Score (MOS) level when the video frames are skipped. If the video frames are not skipped, then it is suggested to avoid freezes during a video stream as the freezes highly increase the energy waste on the smartphones.

As one of the top ten countries that has highest internet users, Indonesia is a land of opportunities for service provider companies to grow their market. Indonesian people also love to use their gadget and grow attach to them. To maintain the customers' satisfaction, the service provider companies need to balance the quality of product and the quality perceived by the customers, especially when using the compression process for sending and receiving the digital image file. This study presented in this paper built the relation between image compressed quality, Quality of Service parameters and Quality of Experience in Mean Opinion Score form. The compression technique used in this paper was Embedded Zerotreee Wavelet with Haar basis with variations in level of decomposition and number of loop. From the research results, we analyzed that when we used level 1, 3 and 5 of decomposition, the results of Mean Opinion Score from 30 respondents was that the respondents could not tell the differences between original image and compressed images. From the Quality of Service parameters results, the compression processes did not interfere with the ability of the network to send and receive the images.

This paper proposes an efficient optimization algorithm to dynamically control the Evolved Packet System (EPS) bearer rates to transport various services between the UE and Evolved Packet Core (EPC) in a LTE Femtocell network scenario. The algorithm is focused on improving the accumulated QoE in the networks and takes joint consideration of limited radio and transport resource so as to leverage the resource management in LTE Radio Access Network (RAN) and transport network. In case of link congestion applications running on TCP tend to share the capacity equally. However, different traffic types have different QoE behaviors and hence sharing the resource equally will lead to a non-optimal aggregated QoE. The proposed algorithm will solve this problem by considering the QoE of individual application flows. We formulate the problem as a convex optimization problem, which maximizes the aggregated QoE, represented by Mean Opinion Score (MOS) value, of all users using different applications, and then solve it using Lagrangian relaxation method. The performance of our algorithms is investigated and evaluated by simulations. The simulations show that our proposed QoE-driven rate shaping algorithm results in a significantly better aggregated QoE compared to the legacy scheme that with fixed rate shaping, especially in heavily congested scenarios. Moreover, a discussion on how often to adjust the shaping rates is given based on the complexity and performance investigations.

Recently, standard of voice quality on “Voice Call” - one of GSM service, is measured by using standard Quality of Service (QoS). However, according to ministerial of Communication and Information regulation (Permenkominfo) 2008, the guaranteed quality of Voice Call service was Endpoint Service Availability, and not the quality delivered to end-users. In this paper, we proposed a subjective method to measure voice quality valued by the experience of the end-users (Quality of Experience). The proposed method focuses on the degradation of voice quality and intelligibility between caller and receiver. Subjective measurement had been performed by using 2 different method, first method emphasized on measurement of ‘Speech intelligibility’ and the second one emphasized on measurement reduction of voice quality. To obtain it, we used value of Percent Correct Words and Degradation Mean Opinion Score (DMOS). The measurement objective was measured by using Perceptual Evaluation of Speech Quality (PESQ). The stimuli were recorded during busy hour (Permenkominfo, 2008), to obtain the greatest distortion possible in the GSM Networks. The result of this study was 98.9% in percent correct words, 4.5 in DMOS, and 3.44 in PSEQ. Although PESQ and DMOS does not have the same weight, but the trend seen from both DMOS and PESQ value was proportional with the correlation coefficient = 0.82. Based on the result of those 3 measurement values, the operator 2 had better voice quality than the operator 1. The experimental results suggest that the quality of the two operators are still in the good category (degradation is audible but not annoying) even in busy business hours.

Nowadays, world is on going to “Convergence Era”, where data, voice, and video overcome to pass through same network, like IP network. Unfortunately, the fast growth of application technology has not been supported yet by default IP network in the case of quality of service. We have known, that voice over IP services have sensitivity to delay, jitter and latency. And it require of different bandwidth which is also required some service guarantee. How to comparing queue mechanism of VoIP attributed to QoS?. In this case, we need to give guarantee of QoS relied on operation of data packages to be delivered along with its scheduling. Scheduling represent queue mechanism from an applying of network QoS's, that is an arrangement process of an exit and entry of assumed data packages queuing exceed CIR (Committed Information Rate) specified. Regulation of this data exit and entry can be formed with specified classification. This research compare queue method of First In First Out (FIFO) with two algorithm type of WFQ scheduling which at most used, that is Class Based Weighted Fair Queuing (CBWFQ) and Low Latency Queuing (LLQ). Based on delay, jitter, and MOS (Mean Opinion Score) obtained, LLQ give a better network performance.

This paper presents a comparative study of VoIP quality over 3G networks, based on Fair Usage Policy (FUP) and Access Unlimited Internet Service (AUIS) provided by two main 3G operators using Skype and a form of mobility usage, defined as semi-dynamic tests. The tests were conducted on electric train called BTS Skytrain (Sukhumvit line). After gathering data of degraded speech files from the tests with Thai speech files, they were processed using a tool called Perceptual Evaluation of Speech Quality tool (PESQ) to obtain the values of Mean Opinion Score (MOS), whereas, data of major Quality of Service (QoS) parameters, consisting of loss, delay and jitter were also gathered. Then, it has been analyzed and found that VoIP quality has been impacted by FUP because QoS parameters and MOS tend to be worse than the values from AUIS.

Femtocells are low power base stations operating in licensed spectrum with the intent to improve coverage and performance of voice and broadband services. The use of data hungry devices such as, tablets, smart phones and other wireless devices has rapidly been increasing in recent years and is driving the need to provide higher speed connections, more capacity and better quality of service. This article presents energy efficient femtocell based architecture for low coverage areas of wimax to improve indoor coverage of cellular mobile devices in the areas where coverage is weak or intermittent. It demonstrates the coverage is significantly improved introducing femtocells in low coverage areas and the total network capacity can be enhanced many folds as compared to macrocell networks. Simulation results show that the proposed architecture performs better in terms of different parameters like Throughput, End-to-End Delay, Voice Traffic Received and Mean Opinion Score (MOS).

The rapid growth of video in wireless networks is a crucial issue to be addressed by content providers. Nowadays, an emerging and promising trend is the development of solutions aimed at maximizing the quality of experience (QoE) of end users. However, the prediction of the QoE perceived by users in different conditions remains a major challenge. In this paper, we propose a two-layer hierarchical QoE model for wireless video streaming systems that employ fountain codes in forward error correction (FEC). The first layer decomposes the impact of fountain codes on the user QoE into three factors, namely the initial playback delay, the rebuffering frequency and the rebuffering time. The second layer of the QoE model relates the user QoE in terms of the mean opinion score (MOS) to the aforementioned three factors using a quadratic function. An exemplary application of the developed QoE model in the admission control of a bandwidth-limited video on demand (VoD) system is given with the goal toward guaranteeing the QoE quality of all users. Simulation results confirms the effectiveness of the developed admission control technique.

The one of challenges in VoIP networks development is how to manage network traffics. The reason for the challenge is VoIP traffics have to use internet networks to bring their messages. Meanwhile, internet is convergent networks which are very busy, because many applications use the internet. The implementation of queuing mechanisms which can distinguish network traffics is the one of mechanisms to manage network traffics. PQ and CBQ are mechanisms which can divide traffics into different classes by using a priority mechanism. In this paper, the analysing of VoIP performance and its QoS to manage its services are executed by PQ and CBQ simulation. The E-model which is introduced by ETSI and standardized by ITU-T through G.107 is used to predict the value of MOS (Mean Opinion Score) services. Then the MOS value results are used to find a better mechanism between PQ and CBQ queue system for managing VoIP traffics.

Resource allocation mechanisms are important in assigning all available resources in the network. Scheduling is one of the resource allocation mechanism which will determine the performance of the network. This paper provides the performance analysis on three different scheduling algorithms in the Long Term Evolution-Advanced downlink transmission system with realistic channel model for urban macro cell scenario. All scheduling algorithms are evaluated based on throughput, frame delay, packet delay variation and mean opinion score index of voice over internet protocol via simulation executed on OMNET++ based simulator. The maximum of carrier to interference ratio scheme provided the highest value of throughput and mean opinion score index among all schemes in the evaluated network. The scheme also provided the lowest value of frame delay and packet delay variation in the evaluated network scenario.

In this paper, we explore user-specific QoS requirements and associated schedulers that are very critical in optimizing the spectral allocation for wireless systems. Two user-specific QoS aware schedulers are proposed that considers the user-specific QoS requirements in the allocation of resources. Depending upon whether improving the MOS (Mean Opinion Score) or both the system capacity and the MOS is the goal, a MOS improvement scheduler or MOS-plus-capacity improvement scheduler is proposed for VoIP applications. Detailed system implementation analysis based upon LTE system specification is performed, and it is shown that very modest modifications to current protocols are needed to support user-specific QoS aware scheduling. System simulations are performed for a set of VoIP users assigned specific QoS target levels in the OPNET Modeler for LTE systems. Simulation results show that appreciable MOS or/and system capacity improvement can be achieved if such user-specific QoS requirements are considered in the proposed user-specific QoS aware schedulers. Also, it is shown that the scheduling period of up to 1000 ms doesn't significantly impair the system performance.