Arghir-Nicolae Moldovan

Energy consumption analysis of video streaming to Android mobile devices

Energy conservation has become a critical issue around the world. In smart phones, battery power capabilities are not keeping up with the advances in other technologies (e.g., processing and memory) and are rapidly becoming a concern, especially in view of the growth in usage of energy-hungry mobile multimedia streaming. The deficiency in battery power and the need for reduced energy consumption provides motivation for researchers to develop energy efficient techniques in order to manage the power consumption in next-generation wireless networks. As there is little analysis in the literature on the relationship between the wireless environment and the mobile device energy consumption, this paper investigates the impact of network-related factors (e.g., network load and signal quality level) on the power consumption of the mobile device in the context of video delivery. This paper analyzes the energy consumption of an Android device and the efficiency of the system in several scenarios while performing video delivery (over UDP or TCP) on an IEEE 802.11g network. The results show that the network load and the signal quality level have a combined significant impact on the energy consumption. This analysis can be further used when proposing energy efficient adaptive multimedia and handover mechanisms.

Quality Utility modelling for multimedia applications for Android Mobile devices

With the advances in mobile technologies, smart mobile computing devices have become increasingly affordable and powerful, leading to a significant growth in both the number of advanced mobile users and their bandwidth demands. Moreover multimedia streaming to these high-end mobile devices has become widespread. However, multimedia applications are known to be resource-hungry and in order to cope with this explosion of data traffic, operators have started deploying different, overlapping radio access network technologies. One important challenge in such a heterogeneous wireless environment is to ensure an Always Best Experience to the mobile user, anywhere and anytime. This paper proposes the Quality Utility, a realistic mapping function of the received bandwidth to user satisfaction for multimedia streaming applications. The Quality Utility is mapped to a Google Nexus One Android Mobile device and validated through objective and subjective tests.

Subjective Assessment of BitDetect—A Mechanism for Energy-Aware Multimedia Content Adaptation

As mobile devices are becoming more compact and powerful and as they start to be increasingly used for accessing power-hungry multimedia streaming applications, there is an increasing need for mechanisms to efficiently manage the limited battery power resources. This is especially important as the battery capacity has not kept up with the power requirements of an increasing number of mobile device features and “always on” connected users. Adaptive multimedia-based power-saving mechanisms often decrease the clip bitrate to increase the mobile device battery life, without considering the effect of these degradations on the user-perceived quality. This paper proposes BitDetect, a mechanism that uses objective video quality assessment metrics to detect content-specific video bitrate levels that enable saving battery power while maintaining good user perceived quality. Results from a subjective study indicate that the recommended bitrate offers good user-perceived quality across different multimedia clips. Furthermore, experimental tests indicate that significant battery power can be saved by using the recommended bitrates when streaming multimedia clips to a mobile device.

User-centered EEG-based multimedia quality assessment

Multimedia users are becoming increasingly quality-aware as the technological advances make ubiquitous the creation and delivery of high-definition multimedia content. While much research work has been conducted on multimedia quality assessment, most of the existing solutions come with their own limitations, with particular solutions being more suitable to assess particular aspects related to users Quality of Experience (QoE). In this context, there is an increasing need for innovative solutions to assess users QoE with multimedia services. This paper proposes the QoE-EEG-Analyser that provides a solution to automatically assess and quantify the impact of various factors contributing to users QoE with multimedia services. The proposed approach makes use of participants frustration level measured with a consumer-grade EEG system, the Emotiv EPOC. The main advantage of QoE-EEG-Analyser is that it enables continuous assessment of various QoE factors over the entire testing duration, in a non-invasive way, without requiring the user to provide input about his perceived visual quality. Preliminary subjective results have shown that frustration can indicate users perceived QoE.

Personalisation of the multimedia content delivered to mobile device users

People using mobile devices for studying multimedia based educational content are often on the move and thus rely solely on their device battery power supply. When battery power runs low, they have to stop their activities, significantly reducing their learning outcomes and their satisfaction. This paper proposes a solution to perform the personalisation of the multimedia educational content, based both on the learner profile and on the available power resources on the device used. The solution aims to increase the battery life without affecting learners quality of experience. Experimental results show that the battery life can be increased by changing streaming related parameters while preliminary subjective tests have assessed their impact on end user perceived quality of the multimedia clip.

A novel methodology for mapping objective video quality metrics to the subjective MOS scale

With the rapid growth in video-based services, and as users are becoming increasingly quality-aware, the reliable estimation of video quality has become extremely important. While a multitude of objective Video Quality Assessment (VQA) metrics with various performance and complexity have been proposed, the nonlinearity of video quality and the lack of clear interpretations of the metrics make difficult to understand how the objective metric values reflect the video quality as perceived subjectively in terms of Mean Opinion Scores (MOS). This paper proposes and evaluates a methodology for mapping objective VQA metric values to subjective MOS scores based on publicly available VQA databases. Three different databases were used for comparing the performance of various objective metrics and evaluating the proposed methodology.

VQAMap: A Novel Mechanism for Mapping Objective Video Quality Metrics to Subjective MOS Scale

The reliable estimation of video quality has become increasingly important with the proliferation of online video services and users becoming more quality aware. A multitude of objective video quality assessment (VQA) metrics with various performance and complexity have been proposed. However, their applicability in real-world scenarios is limited by the lack of clear interpretations of how the metric values reflect the subjective user-perceived video quality. This paper proposes a novel mechanism called VQAMap, that uses data from public VQA databases and enables to automatically create generic rules for mapping the values of objective VQA metrics to the subjective MOS scale (i.e., 1-bad, 2-poor, 3-fair, 4-good, and 5-excellent). An extensive evaluation study of VQAMap was conducted using data from three public VQA databases, considering six objective VQA metrics. The results analysis has shown that VQAMap provides mapping rules with quality estimation accuracy as high as 95%, while the variation in performance being caused by the varying accuracy of the different objective metrics.

Performance evaluation of EMOS model for mapping-based Video Quality estimation

Accurate user-perceived video quality estimation models are increasingly needed with the proliferation of multimedia services. Previous research studies have focused on proposing and evaluating objective Video Quality Assessment (VQA) metrics, without mapping their values to Mean Opinion Scores (MOS). This paper presents a model to compute the estimated user-perceived video quality (EMOS), by combining multiple objective VQA metrics whose continuous values are mapped to discrete scores on the 0 - 5 MOS scale. The results analysis of a subjective video quality assessment study with 60 participants have shown that combining multiple VQA metric mappings can improve the user-perceived quality estimation accuracy up to 98.5%.

Energy consumption analysis of cloud-based video games streaming to mobile devices

Within the growing of the gaming industry and mobile technologies, cloud-based video games streaming to mobile devices gains popularity fast. However, several issues and challenges such as user responsiveness, video quality, service quality, operating cost and energy consumption have to be addressed. Previous research studies did not thoroughly investigate the energy consumption of mobile devices used for cloud gaming. This paper focuses on how mobile device energy consumption is impacted by video game content characteristics, transmission protocol and wireless network type. The results show that game content characteristics impact energy consumption (i.e., up to 47% between games at maximum brightness for OLED screens), UDP is more energy efficient than TCP (i.e., up to 13.6%), while WiFi is more energy efficient than 3G (i.e., up to 40%). The outcome of this study can provide beneficial input for adaptive energy efficient cloud-based video games streaming mechanisms.

User QoE assessment on mobile devices for natural and non-natural multimedia clips

Quality of Experience (QoE) has become an increasing topic of research with the proliferation of multimedia services on mobile devices. Previous research studies have focused on proposing objective video quality assessment (VQA) metrics, and evaluating them on generic video content databases that consist mainly of natural clips such as news, sports and movies. This paper investigates the accuracy of VQA metrics to estimate users QoE for natural and non-natural multimedia clips on mobile devices. The results from a subjective study with 60 participants have shown that well known full-reference VQA metrics such as PSNR, SSIM and VIFp exhibit up to 97% QoE estimation accuracy for non-natural clips, despite not being traditionally recommended for such clips.