Purpose The purpose of this paper is to propose a novel maturity model for health-care cloud security (M2HCS), which focuses on assessing cyber security in cloud-based health-care environments by incorporating the sub-domains of health-care cyber security practices and introducing health-care-specific cyber security metrics. This study aims to expand the domain of health-care cyber security maturity model by including cloud-specific aspects than is usually seen in the literature. Design/methodology/approach The intended use of the proposed model was demonstrated using the evaluation method – “construct validity test” as the paper’s aim was to assess the final model and the output of the valuation. The study involved a literature-based case study of a national health-care foundation trust with an overall view because the model is assessed for the entire organisation. The data were complemented by examination of hospitals’ cyber security internal processes through web-accessible documents, and identified relevant literature. Findings The paper provides awareness about how organisational-related challenges have been identified as a main inhibiting factor for the adoption of cloud computing in health care. Regardless of the remunerations of cloud computing, its security maturity and levels of adoption varies, especially in health care. Maturity models provide a structure towards improving an organisation’s capabilities. It suggests that although several cyber security maturity models and standards resolving specific threats exist, there is a lack of maturity models for cloud-based health-care security. Research limitations/implications Due to the selected research method, the research results may lack generalizability. Therefore, future research studies can investigate the propositions further. Another is that the current thresholds were determined empirically, although it worked for the case study assessment. However, to establish more realistic threshold levels, there is a need for more validation of the model using more case studies. Practical implications The paper includes maturity model for the assessment management and improvement of the security posture of a health-care organisation actively using cloud. For executives, it provides a detailed security assessment of the eHealth cloud to aid in decision making. For security experts, its quantitative metrics support proactive and reactive processes. Originality/value The paper fulfils a recognised requirement for security maturity model focussed on health-care cloud. It could be extended to resolve evolving cyber settings.

The explosion of enhanced applications such as live video streaming, video gaming and Virtual Reality calls for efforts to optimize transport protocols to manage the increasing amount of data traffic on future 5G networks. Through bandwidth aggregation over multiple paths, the Multi-Path Transmission Control Protocol (MPTCP) can enhance the performance of network applications. MPTCP can split a large multimedia flow into subflows and apply a congestion control mechanism on each subflow. Segment Routing (SR), a promising source routing approach, has emerged to provide advanced packet forwarding over 5G networks. In this paper, we explore the utilization of MPTCP and SR in SDN-based networks to improve network resources utilization and end- user's QoE for delivering multimedia services over 5G networks. We propose a novel QoE-aware, SDN- based MPTCP/SR approach for service delivery. In order to demonstrate the feasibility of our approach, we implemented an intelligent QoE- centric Multipath Routing Algorithm (QoMRA) on an SDN source routing platform using Mininet and POX controller. We carried out experiments on Dynamic Adaptive video Steaming over HTTP (DASH) applications over various network conditions. The preliminary results show that, our QoE-aware SDN- based MPTCP/SR scheme performs better compared to the conventional TCP approach in terms of throughput, link utilization and the end-user's QoE.

Video resolution changes in an HTTP adaptive streaming session may negatively affect the viewer's quality of experience. Our goal is, through encoding, to make such resolution changes less noticeable for the viewers. This can be achieved by taking video complexity features into account during the encoding process. In this paper, we compare Constant Bitrate (CBR) versus Constrained Constant Rate Factor (CRF) coding approaches and their effects on the noticeability of video resolution changes. To this end, we conducted a dedicated subjective study with 20 subjects in a quasi -lab environment. Our results suggest that choices for fixed-bitrate encoding have to be improved by deeper analysis of video complexity and resolution change patterns.

Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values.

Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.

HTTP adaptive streaming (HAS) has become the de-facto standard for video streaming to ensure continuous multimedia service delivery under irregularly changing network conditions. Many studies already investigated the detrimental impact of various playback characteristics on the Quality of Experience of end users, such as initial loading, stalling or quality variations. However, dedicated studies tackling the impact of resolution adaptation are still missing. This paper presents the results of an immersive audiovisual quality assessment test comprising 84 test sequences from four different video content types, emulated with an HAS adaptation mechanism. We employed a novel approach based on systematic creation of adaptivity conditions which were assigned to source sequences based on their spatio-temporal characteristics. Our experiment investigates the resolution switch effect with respect to the degradations in MOS for certain adaptation patterns. We further demonstrate that the content type and resolution change patterns have a significant impact on the perception of resolution changes. These findings will help develop better QoE models and adaptation mechanisms for HAS systems in the future.

In recent years, quality of experience (QoE) has been investigated and proved to have both influential factors on user's visual quality and perceptual quality, while the perceptual quality means user's requirement on personalized content should be acquired in optimized quality. That's to say, those segments holding user interested content such as highlights need to be allocated more network resource in a resource-limited streaming scenario. However, all the existing HTTP-based adaptive methods only focus the content-agnostic bitrate adaptation according to limited network resources or energy resource, since they ignored user perceived semantics on some important segments, which suffered less quality on the important segments than on those ordinary ones, so as to hurt the overall QoE. In this paper, we have proposed a new semantic-aware adaptation scheme for MPEG-DASH services, which decides how to preserve bandwidth and buffering time depending on content descriptors for the perceived important content to users. Further, a semantic-aware probe and adaptation (SMA-PANDA) algorithm has been implemented in a DASH client to compare with conventional bitrate adaptions. Preliminary results show that SMA-PANDA achieves better QoE and flexibility on streaming user's interested content on MPEG-DASH platform, and it also aggressively helps user interested content compete more resource to deliver high quality presentation.

In recent years, video streaming traffic has increased exponentially over the Internet. This can be observed through the emergence of various video on demand sites such as Netflix, Amazon movies and others. In parallel, IEEE 802.11 (WiFi) technology has been deployed extensively from home to public areas. Although significant research has been done to address issues on video streaming through WiFi networks, it remains a challenging area. There are still interesting areas to be addressed especially to enhance user satisfaction in terms of Quality of Experience (QoE). After reviewing the existing research in improving video QoE through queuing in wireless environments, this paper propose a predictive packet drop technique to maintain a certain level of QoE based on predictive PSNR value without users' feedback. The proposed mechanism identifies packets as part of video frames and predicts the impact of their delay and loss on the resulting video performance. This reduces the need for client feedback and optimizes the resulting QoE of the delivered video. Therefore based on this information, the proposed algorithm can prioritize the video frames (I-,Por B-Frames) whether to queue or drop in scenarios where bandwidth and limited. This method is evaluated using NS-3 simulator with Evalvid module.

The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs). Real-time quality monitoring, with a focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers.

The development of HDR imaging is seen as an important step towards improving the visual quality of experience (QoE) of the end user in many applications. In practice, Tone-mapping operators (TMOs) provide a useful means for converting a high dynamic range (HDR) image to a low dynamic range (LDR) image in order to achieve better visualization on standard displays. Although mobile devices are becoming popular, the techniques for displaying the content of HDR images on the screens of such devices are still in the early stages. While several studies have been conducted to evaluate TMOs on conventional displays, few studies have been carried out to date to evaluate TMOs on small screen displays, such as those used in mobile devices. In this paper we evaluate, using subjective and objective methods, the most popular Tone-mapping-operators in different mobile displays and resolutions under normal viewing conditions for the end-user. Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays. In general, the larger the mobile resolution, the better the subjective results. We also found clear differences between SSDs and LDRs performances. The best TMO for mobile displays is iCAM06 and for computer displays it is Photographic Reproduction.

Dynamic Adaptive Streaming over HTTP (DASH) or MPEG-DASH is a popular technique that allows video quality adaptation for high quality streaming over the Internet. However, with bandwidth fluctuations, DASH performs poorly due to annoying frequent number of stalls. Software Defined Networking (SDN) has emerged as an attractive technology which has found its way into datacentres. Since one of the main goals of the SDN architecture is to make the network programmable and accelerate network innovation by utilizing its control plane, this paper has used the SDN control plane to propose a video quality management scheme based on the traffic intensity under DASH. Experimental results obtained by using Mininet and OpenDaylight controller have shown that the proposed scheme can significantly reduce the number of frequently annoying stalls and their duration by at least 84% and 94%, respectively. This has been achieved by switching network flows from high to low congested network paths within an SDN architecture.

With advances in wireless technology and the increase in popularity of mobile devices, more and more people now rely on mobile devices for multimedia services (such as video streaming and video calls). A mobile device can be connected and roamed to different networks in heterogeneous wireless networks. The Media Independent Handover (MIH) framework is designed by the IEEE 802.21 group to support seamless vertical handover between different networks. However, how to select an appropriate network from available ones and when to execute the handover remain the key challenges in MIH. This paper proposes a user-centric QoE-driven vertical handover (VHO) framework, based on MIH, which aims to maintain acceptable QoE of different mobile application services and to select an appropriate network based on users' preferences (e.g. on cost). Further a user-centric QoE-driven algorithm is implemented in the proposed framework. Its performance is evaluated and compared with two other VHO algorithms based on Network Simulator 2 (NS2) for video streaming services over heterogeneous networks. The preliminary results show that the proposed algorithm can maintain better QoE and at the same time, take into account user's preference on cost when compared with the other two algorithms.

This paper proposes a QoE-driven LTE downlink scheduling algorithm which is able to maximise the number of users per cell while maintaining an acceptable QoE for VoIP applications. The QoE-driven scheduling algorithm was implemented in LTE-Sim and its performance was compared to common LTE scheduling algorithms. The main difference between QoE-driven scheduling and existing scheduling mechanisms was prioritisation of users for resource allocation by taking into account their QoE requirements for VoIP applications. Preliminary results have shown that QoE-driven LTE downlink scheduling improved the cell capacity by 75% compared to MLWDF and 250% compared to PF and EXP-PF.

The current mobile network has been developed to a heterogeneous network that is coexistence of different wireless networks such as UMTS (3G), WIFI, WIMAX (4G) and LTE (4G). Since the demand of realtime multimedia services has significantly increased in recent years and Quality of Experience (QoE) becomes more and more important to users, any single wireless network is hardly to maintain good QoE of multimedia service at any time and in anywhere for any users. Hence, in order to achieve the goal, it is necessary to converge different wireless networks to take the advantages of different wireless networks. However, even though IEEE 802.21-Media Independent Handover (MIH) provides a framework for supporting seamless vertical handover (VHO), how to select an appropriate network and when to carry out handover are still the key challenges to maintain QoE of multimedia service. This paper addresses a QoE-driven VHO algorithm based on MIH that aims to maintain multimedia service with acceptable QoE and avoid unnecessary handover in the heterogeneous network. The performance evaluation was carried out to compare with default MIH bandwidth-based VHO algorithm on video service over UMTS and WIFI networks in Network Simulator 2 (NS2). The results showed that the QoE-driven VHO algorithm could maintain better QoE of multimedia service than the bandwidth-based VHO algorithm by considering video content, and initiated VHO immediately when the QoE of multimedia service became unacceptable. Furthermore, Block List (BL) and acceptable QoE policy could avoid unnecessary handover effectively.

A new reference-free, objective, video quality prediction model that takes into account video content type to predict the quality of streamed high efficiency video coding (HEVC) encoded video sequences is proposed. Research has shown that for the same encoder settings and network quality of service (NQoS), the video quality differs for different types of video content . This indicates that, in addition to encoder settings and NQoS, there may be other key parameters that impact video quality. In this work, we hypothesized that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, temporal information is extracted from the motion vector (MV) information inherent in the encoded video bitstreams and spatial information is extracted from the quantisation parameter (QP) and the number of bits (Bits) of coded intra (I) and predictive (P) frames to develop a metric that quantifies the content type of different video sequences . The content type metric is subsequently used together with encoding QP setting and network packet loss rate (PLR) to develop a reference -free objective video quality prediction model for streamed HEVC encoded video sequences. This model has an accuracy of 92% when the model predicted values of sequences not used in model derivation are compared with mean opinion score (MOS) obtained through subjective method.

Recently, crowdsourcing has emerged as a cheaper and quicker alternative to traditional laboratory based Quality of Experience (QoE) evaluation for video streaming services. Crowdsourcing is a process of recruiting anonymous members of the public to solve/perform different tasks without supervision. This approach of seeking solutions from the public has been enhanced by ubiquitous internet connectivity, low cost and the ease to recruit workers without geographical restrictions. Although crowdsourcing makes it possible to save cost and reach a large group of people to perform subjective video quality testing, challenges such as validity of results and the trustworthiness of workers still remain unresolved. In this paper, we attempt to address some of these challenges by developing a screening algorithm that is able to determine the validity and trustworthiness of crowdsourcing video QoE evaluation results. This algorithm is based on evaluator's extracted data (e.g. Network IP addresses, network devices and browser information, time spent on the crowdsourcing platform, date and grading patterns). To determine the performance of this algorithm, we carried out a separate controlled laboratory based subjective video quality testing. This test enables us to determine how screened crowdsourcing results correlate with lab based results. Preliminary results show that crowdsourcing results can be improved by up to 59% when the proposed screening algorithm is applied. Moreover, the results support our assertion that crowdsourcing can provide reliable video QoE measurements if proper screening is performed on the test results.

The paper presents novel content-aware adaptation schemes for QoE optimized HTTP-based adaptive video streaming services. In recent years, content type has been investigated and proofed to have a deep influence on user's acceptable quality of experience (QoE). However, most of the existing HTTP-based adaptive video strategies only conduct bitrate switching according to network resources without a consideration of video content types. This may cause a non-optimal network resource allocation which may not achieve the optimized QoE for delivered video streaming services. In this paper, we proposed two content-aware adaptation schemes, named as Content-aware Probe and Adapt (C-PANDA) and Content-aware Dynamic Programming (C-DP), which are able to decide the video bitrate for the next video segment, based on not only the available bandwidth, the existing buffer capacity, but also the video content type. We implemented both content-aware adaptation algorithms in player prototypes based on libdash 3.0 library. Preliminary results show that content-aware adaptation schemes achieve better QoE when compared with conventional PANDA scheme. The proposed schemes are able to optimize QoE according to contents. They can achieve higher than acceptable QoE for most attractive contents such as those with high motion intensity to improve user experience for the most interesting scenes; select same video representations for segments belonging to a scene to avoid quality oscillation; and reserve buffer resource during low motion scenes to avoid stalling for the following high motion scenes. The work will be helpful in designing QoE-aware DASH schemes in the future.

In the past, many video quality assessment methods have been proposed to predict video quality non-intrusively from different application and/or network related parameters. One of the important parameters which have been identified to have a significant impact on video quality is the video content type. To determine video content type, researchers have used different methods such as the extraction of video motion features from the encoded bitstream, the calculation of pixel-wise differences between consecutive frames and human vision systems. In this paper, we developed a new motion amount (MA) metric for defining and determining video content type for the newly released HEVC encoding standard. This metric is based on a combination of the counting number of extracted motion vectors from an encoded bitstream, the magnitude and the complexity of video sequences. Accuracy of the metric was determined by performing a direct correlation between MA, video bitrate and Peak Signal-to-Noise Ratio (PSNR). We also performed a performance comparison with existing pixel-wise approach of defining video content type. Preliminary results show that our metric outperformed the pixel-wise approach of content type definition. Based on the new metric, we further developed an HEVC encoded video quality prediction model that takes into account the MA and the Quantization Parameter (QP). We achieved an accuracy of 96% when our model predicted PSNR values are compared with full reference PSNR measurement.

This paper deals with the performance evaluation of deploying an IMS-based media plane interoperability framework. The 3GPP standards describe two possible operating modes based on either a reactive or a proactive approach. We show that both approaches entail some advantages and drawbacks in terms of signaling overhead and call setup times at different ratios of incompatible calls. A prototype implementation of the required elements was carried out in order to use experimental delay contributions. Additionally, the evaluation focuses on the impact of deploying the interoperability solutions over current UMTS and LTE radio access networks.

This paper analyzes the scenario where multiple users are trying to transmit the same or related content to a common destination through a shared radio access network. A specific aspect of this study is that all users work for a common objective, as it would be the transmission of critical emergency-related media contents to a central operator. We analyze the performance achievable using an LTE cell, with different client-driven autonomous adaptation strategies that take both the network performance and the battery status as input parameters. Remarkable performance results are achieved in terms of number of acceptable uplink video streams in different simulation scenarios. The work would help for designing context-and network-aware adaptation schemes for emergency mobile applications.

The 11 papers in this special issue cover a range of topics and can be logically organized in three groups, focusing on QoE-aware media protection, QoE assessment and modelling, and multi-user-QoE management.

The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

IP Multimedia Subsystem (IMS) offers a framework which enables the provisioning of multimedia services with Quality of Service (QoS) and mobility support across heterogeneous networks. The aim of this paper is twofold. First, to present a new fuzzy logic based Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven for video applications. The scheme was tested and evaluated in the NS2 based simulation access networks of third generation Universal Mobile Telecommunication System (UMTS) networks. Second, to demonstrate the implementation of the proposed adaptation scheme in our developed open Android-based IMS test bed. The test bed was developed to fully understand and manipulate the effects of network conditions on perceptual quality. The SBR adaptive scheme is evaluated in terms of the Mean Opinion Score (MOS). Extensive simulation and test bed results demonstrate the effectiveness of the proposed adaptation scheme especially at UMTS bottleneck access networks where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

Mobile/wireless multimedia applications (e.g., video calls and IPTV) have gained great momentum in recent years. An important issue is to monitor/predict overall audiovisual quality, instead of audio-only or video-only quality, non-intrusively for technical or commercial reasons. Previous audiovisual modeling research mainly considered application parameters (e.g., codec and send bit rate). Little attention has been paid to how network parameters, e.g., Packet Error Rate (PER) affect audiovisual quality. The aim of this paper is to explore methods to predict audiovisual quality objectively for video calls in wireless applications. The contributions of the paper are twofold. Firstly, we present subjective test results on how audio and video contribute to overall audiovisual quality and develop models to reflect this relationship. Secondly, we investigated how network parameters (e.g., PER) and application parameters, e.g., video Frame Rate (FR) affect overall audiovisual quality. We developed a regression model to predict audiovisual quality from PER and FR which can be used to monitor/predict audiovisual quality non-intrusively. We also explore the possibility to predict audiovisual quality from full-reference voice and video quality metrics (i.e., PESQ and PSNR) and from combined PESQ/PSNR and network/application parameters. The different predication accuracy obtained from these models (accuracy from 84% to 93%) indicates the complex attributes in audiovisual quality prediction. An extended Evalvid/NS-2 platform is developed to support simulation of video calls over wireless networks.

The Next Generation Networks (NGNs) were proposed to provide ubiquitous access to multimedia services over the Internet. With the best effort nature of the Internet, the quality of real time multimedia services such as voice and video can rapidly degrade due to network impairments. In this paper, we propose a novel feedback-free Early VoIP Quality Adaptation Scheme (EVQAS) which can adapt voice send bit rate (e.g. Adaptive Multi-Rate (AMR) codec mode) automatically according to the buildup of queuing delay in the network. This differs from existing adaptation methods which adapt send bit rate according to either packet loss information or Perceived Quality of Service (PQoS) metrics (e.g. Mean Opinion Score (MOS)) derived from packet loss and delay. The proposed scheme can avoid congested packet loss which has an obvious impact on perceived voice quality. We implemented this adaptation mechanism in open Android mobile and tested in Open IMS Core testbed. Preliminary results show that the early adaptation mechanism can effectively release congestion before packet loss occurs. This has increased overall perceived voice quality when compared with existing adaptation method. This lightweight feedback-free early adaptation mechanism can be easily applied to other mobile devices to improve VoIP quality in current NGN VoIP applications.

Universal Mobile Telecommunication System (UMTS) is a third generation mobile communication systems that supports wireless wideband multimedia applications. The objective of this paper is to present a new model for non-intrusive prediction of H.264 encoded video quality over UMTS networks and to illustrate their application to video quality monitoring and adaptation in mobile wireless streaming services. First, we present an efficient regression model for predicting video quality non-intrusively for all content types. The model is predicted from a combination of a set of objective parameters in the application and physical layer in terms of the Mean opinion Score (MOS). The application layer parameters considered are the content type, sender bitrate and frame rate and the physical layer parameters are the block error rate modeled with 2-state Markov model for a mean burst length of 1.75. The performance of the proposed metric is evaluated with unseen dataset with good prediction accuracy. Second, we illustrate the application of the model in mobile streaming services by presenting a new Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven. The scheme was tested and evaluated in the NS2 based UMTS simulation network. Extensive simulation results demonstrate the effectiveness of the proposed adaptation scheme in terms of the MOS and especially at the UMTS network bottleneck access where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

In Universal Mobile Telecommunication System (UMTS) Radio Link Control (RLC) losses severely affect the Quality of Service (QoS) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour. In this paper we evaluate the impact of the radio access network on the end-to-end QoS for H.264 encoded video. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed that takes into account the RLC loss models to predict the video quality in terms of the Mean Opinion Score (MOS). The RLC loss models considered are 2-state Markov models with variable mean burst lengths. The aim of the paper is two-fold. First, to find the impact of QoS parameters in both the physical and application layer on end-to-end video quality. Second, to propose a prediction model based on ANFIS to predict video quality over UMTS networks. ANFIS is well suited for video quality prediction over error prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS model is trained with a combination of application and physical layer parameters. The performance of the proposed model is validated with unseen dataset. These studies should help in the understanding of the impact of both the application and physical layer parameters on end-to-end video quality and in QoS control methods and adaptation.

Universal mobile telecommunication system (UMTS) is a third-generation mobile communications system that supports wireless wideband multimedia applications. The primary aim of this study is to present learning models based on neural networks for objective, non-intrusive prediction of video quality over wireless local area network (WLAN) and UMTS networks for video applications. The contributions of this study are two-fold: first, an investigation of the impact of parameters both in the application and physical layer on end-to-end video quality is presented. The parameters considered in the application layer are content type (CT), sender bitrate (SBR) and frame rate (FR), whereas in the physical layer block error rate (BLER) and link bandwidth (LBW) are considered. Secondly, learning models based on adaptive neural fuzzy inference system (ANFIS) are developed to predict the visual quality in terms of the mean opinion score for all contents over access networks of UMTS and WLAN. ANFIS is well suited for video quality prediction over error-prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS-based artificial neural network is trained using a combination of physical layer parameters such as BLER and LBW and application layer parameters of CT, SBR and FR. The proposed models are validated using unseen data set. The preliminary results show that good prediction accuracy was obtained from the models. This study should help in the development of a reference-free video prediction model and quality of service control methods for video over UMTS/WLAN networks.

User's perceived quality of service (QoS) or quality of experience (QoE) is likely to be the major determining factor in the success of new multimedia applications over wireless/mobile networks. The primary aim of this study is to present an adaptation scheme that is QoE-driven for optimising content provisioning and network resource utilisation for video applications over wireless networks. The proposed scheme encompasses the application of a QoE-driven model for optimising content provisioning and network resource utilisation. The content provisioning is optimised by the determination of initial content quality by adapting the video sender bitrate (SBR) according to users' QoE requirement. By finding the impact of the QoS parameters on end-to-end perceptual video quality, the optimum trade-off between SBR and frame rate is found and the benefits to network providers in maximising existing network resources are demonstrated. The QoE is measured in terms of the mean opinion score. The proposed scheme makes it possible for content providers to achieve optimum streaming (with an appropriate SBR) suitable for the network and content type for a requested QoE. The scheme is also beneficial for network providers for network resource provision and planning, and therefore maximising existing network infrastructure by providing service differentiation.

The aim of this paper is quality prediction for streaming MPEG4 video sequences over wireless networks for all video content types. Video content has an impact on video quality under same network conditions. This feature has not been widely explored when developing reference-free video quality prediction model for streaming video over wireless or mobile communications. In this paper, we present a two step approach to video quality prediction. First, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, based on the content type, video quality (in terms of Mean Opinion Score) is predicted from network level parameter (packet error rate) and application level (i.e. send bitrate, frame rate) parameters using Principal Component Analysis (PCA). The performance of the developed model is evaluated with unseen datasets and good prediction accuracy is obtained for all content types. The work can help in the development of reference-free video prediction model and priority control for content delivery networks.

PESQ is the international standard for objective speech quality measurement and has been widely used in industry for voice quality assessment for VoIP products and systems. However, current research efforts involving PESQ are mainly focused on simulated IP networks. The need remains to establish how accurate PESQ is when used in real wireless systems, especially in new wireless VoIP environment. The main objective of this paper is to investigate the accuracy of PESQ measurement in live wireless mobile environment. We set up a PESQ performance evaluation platform and tested end-to-end voice quality for over 1800 mobile-to-mobile and mobile-to-PSTN calls over a period of 3 months. We found that there still exist some accuracy problems when using PESQ in mobile VoIP environment. These problems are mainly caused by improper time alignment in the PESQ algorithm when there are silence gap and speech sample removal or insertion due to packet loss concealment and jitter buffer adjustment in mobile devices. In the paper, we illustrate these problems in a detailed case study and provide suggestions for improvement of test performance.We also discussed the importance of calibration issues (e.g. volume optimization) for a safe and proper PESQ testing in mobile environment. The outcome of the study will be helpful in understanding problems associated with voice quality measurements in live mobile VoIP environment and how to further improve PESQ accuracy in mobile VoIP systems.

The quality of communications in mobile ad-hoc networks is largely determined by the topological stability. Characterizing the mobility of mobile nodes (e.g. how often they move away from each other) would enable the measurement of the frequency of network reconfigurations for predicting stability. In this paper, we first define a triplet (V, ominus, psi) which consists of mobility parameters that are directly correlated with the stability of a network (e.g. inter-node relative speeds/orientations and epoch time). Based on our previous study on range-based velocity estimations, we then propose a new scheme to estimate the mobility parameters in realtime. In contrast to existing methods that are based on either localization systems or features of wireless channel, the proposed scheme estimates the parameters (V, ominus, psi) only from the time-varying inter-node distance information while retaining its reliability and accuracy even in noisy environments. The performance of the proposed scheme is validated in computer simulations against an existing method under different network settings. We also apply the proposed scheme to link availability estimation as a more lightweight way of obtaining mobility parameters using an existing tool. Preliminary results for various scenarios show that, with the proposed scheme, the tool achieved a comparable performance to when a priori knowledge of nodal mobility is used.

Efficient mobility metrics are necessary for mobile ad hoc networks to measure the impact of node mobility on performance. Normally, measuring mobility requires the use of complex localisation systems. A new mobility metric for performance measurements, the intra-vicinity dependency, is proposed. Its main novelty is that it can fully capture the relative motions between a node and its vicinity in a 2D plane, in real-time, using simple triangulation. Variants of this metric are proposed for predicting the performance of networks that follow group and random mobility models (e.g. inter-group inter-meeting times and packet delivery rate). To make the proposed mobility metrics more robust in noisy environments, a calibration method is also proposed for improving accuracy. Experimental results show that, without the help of any localisation systems, the proposed metrics enable a more accurate approximation of the average relative speed between mobile nodes/groups than existing methods. It is also shown that the proposed metrics yield excellent performance when they are used to predict the inter-group inter-meeting times for networks that follow the Rereence Point Group Mobility model and to estimate the packet delivery rate for those that follow the Random Way Point model.

Intermittently connected mobile ad hoc networks (ICMANs) are mobile networks in which complete source-to- destination paths do not exist most of the time because of their sparse topology. Such networks enable communications among sparsely distributed moving objects, e.g. a network of ambulances. Traditional routing schemes are unsuitable in ICMANs because they only forward packets in connected paths. Recently, store-and-forward based routing (e.g. multi-copy relaying) have been proposed to address this. However, existing multi-copy strategies do not adapt to the frequent variations in network conditions as they rely heavily on the source to control the relaying process. In this paper, we propose an efficient store-and- forward based scheme, adaptive multi-copy routing (AMR), for packet delivery in ICMANs. In this scheme, instead of relying on the source, each relay node independently decides whether to forward a packet according to the delivery delay target and current network conditions. Simulation results show that the proposed scheme always adapts to the varying network mobility and/or delivery probability that cannot be anticipated in the conventional source-defined strategies and delivers packets within specific delay targets with minimum traffic overhead.

Service discovery is a challenging task in mobile ad-hoc networks which are characterized by their dynamic, infrastructure-less and bandwidth-limited nature. The goal of this paper is to develop a bandwidth efficient approach for service discovery in mobile ad-hoc networks. The main contribution of this paper is a novel track-based hybrid service discovery scheme. The proposed scheme performs service discovery based on the one dimensional proactive structure (e.g. tracks), instead of the existing two dimensional proactive structures (e.g. zones). This scheme can lead to reduced proactive traffic due to its light-weight organization of the hybrid structure. The density of proactive tracks is adapted to network traffic pattern dynamically for less proactive traffic. We simulated the Track-based approach in ns-2 network simulator with various network scales and mobility, and compared the performance with pure reactive (e.g. flooding) and Zone-based hybrid (e.g. bordercasting) protocols. Preliminary results show that, the track:-based approach achieved better efficient trade-offs between control packet overhead and querying success rate with minimal proactive costs among all the approaches compared

Quality of service (QoS) control is an important issue in voice over IP (VoIP) applications because of the need to meet technical and commercial requirements. The main objective of this paper is to propose a new QoS control scheme that combines the strengths of adaptive rate and speech priority marking QoS control techniques to provide a superior QoS control performance, in terms of perceived speech quality. A second objective is to propose the use of an objective measure of perceived speech quality (i.e. objective MOS score) for adaptive control of sender behaviour as this provides a direct link to user-perceived speech quality, unlike individual network impairment parameters (e.g. packet loss and/or delay). Our results show that the new combined QoS control method achieved the best performance under different network congestion conditions compared to separate adaptive sender rate or packet priority marking method. Our results also show that the use of an objective MOS as the control parameter for the sender rate adaptation improves the overall perceived speech quality. The results reported here are based on a simulation platform that integrates DiffServ enabled NS-2 network simulator, a real speech codec (AMR codec) and the ITU-T standard speech quality evaluation tool (PESQ).

Perceived voice quality is an important metric in VoIP applications. The quality is mainly affected by network impairments such as delay, jitter and packet loss. Playout buffer at the receiving side can be used to compensate for the effects of jitter based on a tradeoff between delay and loss. The main aim in this paper is to find an efficient perceived quality prediction method for perceptual optimization of playout buffer. The contributions of the paper are three-fold. First, we propose an efficient new method for predicting voice quality for buffer design/optimization. The method can also be used for voice quality monitoring and for QoS control. In the method, nonlinear regression models are derived for a variety of codecs (e.g. G.723.1/G.729/AMR/iLBC) with the aid of ITU PESQ and the E-model. Second, we propose the use of minimum overall impairment as a criterion for buffer optimization. This criterion is more efficient than using traditional maximum mean opinion score (MOS). Third, we show that the delay characteristics of voice over IP traffic is better characterized by a Weibull distribution than a Pareto or an exponential distribution. Based on the new voice quality prediction model, the Weibull delay distribution model and the minimum impairment criterion, we propose a perceptual optimization buffer algorithm. Preliminary results show that the proposed algorithm can achieve the optimum perceived voice quality compared with other algorithms under all network conditions considered.

Perceived conversational speech quality is a key quality of service (QoS) metric for voice over IP (VoIP) applications. Speech quality is mainly affected by network impairments, such as delay, jitter and packet loss. Playout buffer algorithms are used to compensate for jitter based on a tradeoff between delay and loss, but can have a significant effect on perceived quality. The main aim in this paper is to assess how buffer algorithms affect perceived speech quality and how to choose the best algorithm and its parameters to obtain optimum perceived speech quality (in terms of an objective mean opinion score). The contributions of the paper are three-fold. First, we introduce a new methodology for predicting conversational speech quality (conversational mean opinion score or MOSc) which combines the latest ITU-T speech quality measurement algorithm (PESQ) and the concepts of the E-model. Second, we assess different playout buffer algorithms using the new MOSc metric on Internet trace data. Our findings indicate that, in general, end-to-end delay has a major effect on the selection of a buffer algorithm and its parameters. For small end-to-end delays, an algorithm that seeks to minimise loss is preferred, whereas for large end-to-end delays, an algorithm that aims at a minimum buffer delay is best. Third, we propose a modified buffer algorithm together with an adaptive parameter adjustment scheme. Preliminary results show that this can achieve an "optimum" perceived speech quality for all the traces considered. The results are based on Internet trace data measurements between UK and USA, UK and China, UK and Germany.

Perceived speech quality is the key metric for QoS in VoIP applications. Our primary aims are to carry out a fundamental investigation of the impact of packet loss and talkers on perceived speech quality using an objective method and, thus, to provide the basis for developing an artificial neural network (ANN) model to predict speech quality for VoIP. The impact on perceived speech quality of packet loss and of different talkers was investigated for three modern codecs (G.729, G.723.1 and AMR) using the new ITU PESQ algorithm. Results show that packet loss burstiness, loss locations/patterns and the gender of talkers have an impact. Packet size has, in general, no obvious influence on perceived speech quality for the same network conditions, but the deviation in speech quality depends on packet size and codec. Based on the investigation, we used talkspurt-based conditional and unconditional packet loss rates (which are perceptually more relevant than network packet loss rates), codec type and the gender of the talker (extracted from decoder) as inputs to an ANN model to predict speech quality directly from network parameters. Results show that high prediction accuracy was obtained from the ANN model (correlation coefficients for the test and validation datasets were 0.952 and 0.946 respectively). This work should help to develop efficient, nonintrusive QoS monitoring and control strategies for VoIP applications.