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Interactive visual presentation of information can help an analyst gain faster and better insight from data. When combined with situational or context information, visualization on mobile devices is invaluable to in-field responders and investigators. However, several challenges are posed by the form-factor of mobile devices in developing such systems. In this paper, we classify these challenges into two broad categories - issues in general mobile computing and issues specific to visual analysis on mobile devices. Using NetworkVis and Infostar as example systems, we illustrate some of the techniques that we employed to overcome many of the identified challenges. NetworkVis is an OpenVG-based real-time network monitoring and visualization system developed for Windows Mobile devices. Infostar is a flash-based interactive, real-time visualization application intended to provide attendees access to conference information. Linked time-synchronous visualization, stylus/button-based interactivity, vector graphics, overview-context techniques, details-on-demand and statistical information display are some of the highlights of these applications.

HTTP Adaptive Streaming(HAS) has become widely deployed in multimedia services for its ability for providing the best Quality of Experience(QoE) for viewers with limited resource, especially in fluctuating wireless networkings. With its client-driven rate adaptation mechanism, HAS is able to adapt to fluctuating network condition and various terminals dynamically. The rate adaptation algorithm is crucial in HAS for its direct impact on the QoE. Most of researches focus on optimizing objective indicators, such as elevating mean bit-rate, lessening playout interruption, and avoiding quality switches, etc. However, the promotion in objective indicators doesn't indicate better experience of end users directly. In this paper, a novel QoE-Optimal Rate Adaptation(QORA) algorithm is proposed to achieve the best QoE. Firstly, a QoE model with iterative form is deduced for on-line evaluation. According to this model, the QORA is able to predict the possible QoE of each bit-rate level in candidate, then greedily choose the one with the highest QoE. The perceptual quality of HAS is optimized by the QORA directly and quantitatively. The simulation result shows that the proposed QORA is able to provide the optimal QoE compared with other rate adaptation schemes, although some of them have better performance in a single objective indicator.

Wireless communication networks span a wide range of applications such as traditional telephony, internet data access, and cyber-physical infrastructure monitoring. However, the quality of service is still lacking, and problems such as dropped calls, dead spots, and slow network speed are still very common. Those are due to fundamental constraints such as the scarcity of radio-frequency spectrum, signal propagation effects, e.g., channel fading and shadowing, and the small form factor of mobile devices with limited energy capacity and antenna diversity.

The massive growth of data consumption and the variety of wireless technology emergence has made the handover (HO) an attractive research topic nowadays, mainly due to the popularity of Wireless Local Area Networks (WLANs), which allow users to reach high-speed data communication while they are in movement. Moreover, mobile devices such as tablets and smartphones have also become increasingly popular due to their low cost and ease of use, and an increase in mobile device use is expected to accelerate in the coming years, along with the availability and use of applications such as real-time services and online gaming. The traditional HO methods will likely not meet the requirements of mobile devices for modern applications due to the lack of intelligence, lack of awareness Quality of Service (QoS) and Quality of Experience (QoE) requirements of mobile users. We, therefore, introduce a novel architecture that supports horizontal HO in homogenous networks. This architecture is based on the Software-Defined Wireless Networking (SDWN) concept, where the wireless network is controlled centrally and the wireless Access Points (APs) are programmable. In this architecture, HO algorithms will assist wireless users to find the network that could best support the application requirements through Quality of Service (QoS) and Quality of Experience (QoE) management policies. The first HO algorithm proposed in this thesis is called Quality of Experience Oriented Handover Algorithm. This algorithm will guarantee the best possible connectivity to the users in terms of their QoE and QoS requirements and outperforms the traditional methods in a sparse network environment. The second contribution is called Optimised Handover Algorithm for Dense WLAN Environments. This algorithm has been designed to address dense network environments via taking into consideration the Adaptive Hysteresis Value (AHV). The AHV will help the Optimised Handover Algorithm via reducing the so-called ping-pong effect. This contribution shows promising performance results by selecting the best candidate AP, decreasing the number of redundant HO and avoiding the ping-pong effect. The final contribution is called Priority Based Handover Algorithm. We extended our proposed SDWN architecture in order to include the concept of prioritising users and make a smart decision during the process of HO. This algorithm will prioritise a certain class of users to avoid the effect of the over-congestion. The results show that the approach based on priority outperforms the state of the art and provides better QoE to the high priority users despite the over-congestion situation.

<p>IEEE 802.11 technology provides a low-cost wireless networking solution. In the last few years, we have seen that the demand for high-bandwidth wireless local area networks increases rapidly, due to the proliferation of mobile devices such as laptops, smart phones and tablet PCs. This has driven the widespread deployment of IEEE 802.11 wireless networks to provide Internet access. However, wireless networks present their own unique problems. Wireless channel is extremely variable and can be affected by a number of different factors, such as collisions, multipath fading and signal attenuation. As such, rate adaptation algorithm is a key component of IEEE 802.11 standard which is used to vary the transmission data rate to match the wireless channel conditions, in order to achieve the best possible performance. Rate adaptation algorithm studies and evaluations are always hot research topics. However, despite its popularity, little work has been done on evaluating the performance of rate adaptation algorithms by comparing the throughput of the algorithm with the throughput of the fixed rates. This thesis presents an experimental study that compares the performance ofMikroTik rate adaptation algorithm andMinstrel rate adaptation algorithm against fixed rates in an IEEE 802.11g network. MikroTik and Minstrel rate adaptation algorithm are most commonly used algorithm around the world. All experiments are conducted in a real world environment in this thesis. In a real world environment, wireless channel conditions are not tightly being controlled, and it is extremely vulnerable to interference of surrounding environment. The dynamic changes of wireless channel conditions have a considerable effect on the performance of rate adaptation algorithms. The main challenge of evaluating a rate adaptation algorithm in a real world environment is getting different experiment behaviours from the same experiment. Experiment results may indicate many different behaviours which due to the leak of wireless environment controlling. Having a final conclusion from those experiment results can be a challenge task. In order to perform a comprehensive rate adaptation algorithm evaluation. All experiments run 20 times for 60 seconds. The average result and stand deviation is calculated. We also design and implement an automation experiment controlling program to help us maintain that each run of experiment is following exactly the same procedures. In MikroTik rate adaptation algorithm evaluation, the results show in many cases that fixed rate outperforms rate adaptation. Our findings raise questions regarding the suitability of the adopted rate adaptation algorithm in typical indoor environments. Furthermore, our study indicates that it is not wise to simply ignore fixed rate. A fine selection of a fixed rate could be made to achieve desired performance. The result ofMinstrel rate adaptation evaluation show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves frombad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality. By comparing the experimental results between the performance of rate adaptation algorithms and the performance of fixed data rate against different factors, the experiment results directly pointed out the weakness of these two rate adaptation algorithms. Our findings from both experiments provide useful information on the design of rate adaptation algorithms.</p>

Thinner displays are continuously developing for reducing form factor of mobile devices. To manufacture the display be thin, touch screen panel layers are directly patterned on top of the display active layers generating huge capacitive coupling between them. Therefore, the touch screen controller (TSC) have to stimulate over heavily coupled touch sensing channels and endure heavily affected noise from the display operation. To overcome adverse effects of recent incorporated touch-display panel development, this paper presents an analog front-end (AFE) of a TSC with the adiabatic charge recycling driver to effectively generate stimuli. The AFE adopts correlated-noise-sampling-embedded modulation on top of fully differential filtering stages overcoming adverse effects of continually developing ultra-thin mobile displays. Fabricated in a 0.18μm CMOS process, the adiabatically driven AFE provides a signal-to-noise-ratio (SNR) of 51.5dB with a 4mmφ touch at 120Hz frame rate and achieves a 14.7dB SNR improvement via noise sampling. The AFE consumes 23.7mW and occupies an active area of 9.1mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Quality of Experience (QoE) is referred as level of user's satisfaction, enjoyment, learning and evaluation about the services or products. Recently quality of experience is used for improvement in product development life cycle after getting feedback from end users and service providers also use QoE to measure quality of services (QoS) of their services. Cloud computing provides services such as storage, web hosting, operating system environment, application development platform and CPU resources pay per use. End users are accessing cloud services via mobile apps, but enormous amount of temporary/cache data is generated by apps, so internal storage of mobile devices are filled quickly. Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services, which degrade the QoE of end users for particular cloud app and services. This paper presents the results of experiments conducted using two mobile devices HTC and Samsung to analyze the impact on end user's QoE during accessing cloud, when internal storage of HTC mobile device is filled and Samsung having 10 GB free space. Finally, on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user's QoE.

The 13 articles in this special section focus on artificial intelligence and cognitive wireless communications. With the rapid development of wireless communication and networking technologies, novel information services and applications are booming globally. Advanced communications and networks greatly enhance users’ experience and have had a significant impact on all aspects of people’s lives, including at home, at work, in social exchanges, and economically. Although these advanced techniques have extensively improved users’ quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide area coverage, high-capacity hotspot, low-power massive connections, low latency and high reliability, and other scenarios. Under the new service paradigm, artificial intelligence (AI) is significant for cognitive wireless communications to meet various technical challenges. The application potential includes complicated decision making, wireless network management, resource optimization, and in-depth knowledge discovery in complex wireless networking environments. These articles present a comprehensive view of research challenges and opportunities regarding AI for cognitive wireless communications in order to cope with the extreme demands of user experience, efficiency, and performance in a complex wireless networking environment.

Dynamic adaptive streaming over HTTP (DASH) is gaining popularity for video streaming over the Internet. DASH uses a rate adaptation algorithm to adapt the bitrate played continuously based on the network conditions. Most of the algorithms use buffer occupancy, throughput, or a combination thereof to adapt the bitrate. In the literature, the Quality of Experience (QoE) of users is measured objectively using metrics such as, number of interruptions and their duration, average bitrate played, and the number of bitrate switches. In this work, we propose a score-based approach to assess the user experience of different rate adaptation algorithms objectively. We experimented with five algorithms implemented using an HTML5 player under varying bandwidth and network conditions. Our results demonstrate that algorithms considering both measured bandwidth and playback buffer occupancy lead to better QoE even under low bandwidth conditions. We show that buffer-based algorithms do not work well in networks with low bandwidth. Algorithms that only try to improve the bitrate played often end up with more interruptions or bitrate switches when bandwidth fluctuates. Due to the mutual dependency among the objective metrics, most of the algorithms do not necessarily improve overall QoE while selecting appropriate bitrate.

The rapid uptake of intelligent applications is pushing deep learning (DL) capabilities to mobile devices. However, the heterogeneities in device capacity, DNN performances, and user preferences make it challenging to provide satisfactory Quality of Experience (QoE) to mobile users. This paper studies automated customization for DL inference on mobile devices (termed as on-device inference), and our goal is to enhance user QoE by configuring the on-device inference with an appropriate DNN for users under different usage scenarios. The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-device inference with the learned knowledge. It leverages an online learning algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NeuralUCB</i> , that has excellent generalization ability for handling various user QoE patterns. We also embed the knowledge transfer technique in NeuralUCB to expedite the learning process. However, NeuralUCB frequently solicits QoE ratings from users, which incurs non-negligible inconvenience. To address this problem, we design feedback solicitation schemes to reduce the number of QoE solicitations while maintaining the learning efficiency of NeuralUCB. A pragmatic problem, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregated QoE</i> , is further investigated to improve the practicality of our framework. We conduct experiments on both synthetic and real-world data. The results indicate that our method efficiently learns the user QoE pattern with few solicitations and provides drastic QoE enhancement for mobile devices.

Recently, the evaluation on quality of the air interface in a wireless network has shifted from emphasis on QoS (quality of service) only to incorporation of QoE (quality of experience). However, it seems that QoE metrics have not been related to the surroundings of the served users so far, which may have a great influence on users' expected QoE. For instance, with a high level of QoS, a user's expectation of QoE could still be terrible because of the serious interference from the surroundings such as noise, ambient light or shaking of terminals due to mobility. From another perspective, it means that in such a case a high level of QoS may be helpless to enhance users' QoE and the requirement could be relaxed accordingly. In this paper, an approach called surrounding-adaptive QoE adjustment (SAQA) is proposed to optimize the capacity and the quality of air interface with voice service as an example. The basic idea of the approach is that user equipment (UE) detects the surroundings interference which may impact users' QoE and then adjusts QoS according to the detected results: a lower level of QoS is chosen in a bad environment to enhance the capacity, while a higher level of QoS is selected to guarantee the users' QoE in a better circumstance. Simulation results show that this method can remarkably enhance the voice capacity of the communication systems as well as guarantee users' QoE.

HTTP adaptive streaming (HAS) is a state-of-the-art technology for video streaming. Optimal adaptive streaming schemes should be designed to maximize the quality of experience (QoE) in network environments with unstable bandwidths such as wireless networks. QoE is degraded for Advanced Video Coding (AVC)-based HAS due to frequent quality switching and rebuffering in wireless networks. Meanwhile, Scalable Video Coding (SVC)-based HAS can maximize the QoE by flexibly requesting the base layer (BL) and enhancement layer (EL) based on variable network conditions. However, in SVC-based HAS, the required bandwidth is significantly high due to the encoding overhead. In this paper, we propose a layer-assisted video quality adaptation for improving QoE in wireless networks. The proposed scheme employs a video encoding method, composed of multiple BLs and ELs, using both AVC and SVC. The quality of the BL is determined using the buffer occupancy level and measured bandwidth to minimize rebuffering. The quality of the EL is determined using the buffer region and segment quality differences to minimize instability. enhancement dummy (ED) consists of the selected ELs. The layer scheduler decides the segment that is to be requested based on the QoE model according to the BL and ED to maximize the QoE. Experimental results show that the proposed scheme achieves high QoE compared to the existing schemes because the instability is low and no rebuffering occurs.

4 - QoE-based Power Efficient LTE Downlink Scheduler

Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide today. Therefore, recent efforts have started to target efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis, we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

While previous expensive and complex desktop video conferencing solutions had a restricted reach, the emergence of the WebRTC (Web Real-Time Communication) open framework has provided an opportunity to redefine the video conferencing communication landscape. In particular, technological advances in terms of high resolution displays, cameras, and high speed wireless access networks have set the ground for emerging multiparty video telemeeting solutions realized via mobile devices. However, deploying multiparty video communication solutions on smart phones calls for the need to optimize video encoding parameters due to limited device processing power and dynamic wireless network conditions. In this paper, we report on a subjective user study involving 30 participants taking part in three-party audiovisual telemeetings on mobile devices. We conduct an experimental investigation of the Google Congestion Control (GCC) Algorithm in light of packet loss and under various video codec configurations, with the aim being to observe the impact on end user Quality of Experience (QoE). Results provide insights related to QoE-driven video encoding adaptation (in terms of bit rate, resolution, and frame rate), and show that in certain cases, adaptation invoked by GCC leads to video interruption. In majority of other cases, we observed that it took approximately 25 s for the video stream to recover to an acceptable quality level after the temporary occurrence of network packet loss.