Subjective quality assessment of longer duration video sequences delivered over HTTP adaptive streaming to tablet devices

HTTP adaptive streaming facilitates video streaming to mobile devices connected through heterogeneous networks without the need for a dedicated streaming infrastructure. By splitting different encoded versions of the same video into small segments, clients can continuously decide which segments to download based on available network resources and device characteristics. These encoded versions can, for example, differ in terms of bitrate and spatial or temporal resolution. However, as a result of dynamically selecting video segments, perceived video quality can fluctuate during playback which will impact end-users' quality of experience. Subjective studies have already been conducted to assess the influence of video delivery using HTTP Adaptive Streaming to mobile devices. Nevertheless, existing studies are limited to the evaluation of short video sequences in controlled environments. Research has already shown that video duration and assessment environment influence quality perception. Therefore, in this article, we go beyond the traditional ways for subjective quality evaluation by conducting novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. As such, we want to mimic realistic viewing behavior as much as possible. Our results show that both video content and the range of quality switches significantly influence end-users' rating behavior. In general, quality level switches are only perceived in high motion sequences or in case switching occurs between high and low quality video segments. Moreover, we also found that video stallings should be avoided during playback at all times

Quality-aware Content Adaptation in Digital Video Streaming

User-generated video has attracted a lot of attention due to the success of Video Sharing Sites such as YouTube and Online Social Networks. Recently, a shift towards live consumption of these videos is observable. The content is captured and instantly shared over the Internet using smart mobile devices such as smartphones. Large-scale platforms arise such as YouTube.Live, YouNow or Facebook.Live which enable the smartphones of users to livestream to the public. These platforms achieve the distribution of tens of thousands of low resolution videos to remote viewers in parallel. Nonetheless, the providers are not capable to guarantee an efficient collection and distribution of high-quality video streams. As a result, the user experience is often degraded, and the needed infrastructure installments are huge. Efficient methods are required to cope with the increasing demand for these video streams; and an understanding is needed how to capture, process and distribute the videos to guarantee a high-quality experience for viewers. This thesis addresses the quality awareness of user-generated videos by leveraging the concept of content adaptation. Two types of content adaptation, the adaptive video streaming and the video composition, are discussed in this thesis. Then, a novel approach for the given scenario of a live upload from mobile devices, the processing of video streams and their distribution is presented. This thesis demonstrates that content adaptation applied to each step of this scenario, ranging from the upload to the consumption, can significantly improve the quality for the viewer. At the same time, if content adaptation is planned wisely, the data traffic can be reduced while keeping the quality for the viewers high. The first contribution of this thesis is a better understanding of the perceived quality in user-generated video and its influencing factors. Subjective studies are performed to understand what affects the human perception, leading to the first of their kind quality models. Developed quality models are used for the second contribution of this work: novel quality assessment algorithms. A unique attribute of these algorithms is the usage of multiple features from different sensors. Whereas classical video quality assessment algorithms focus on the visual information, the proposed algorithms reduce the runtime by an order of magnitude when using data from other sensors in video capturing devices. Still, the scalability for quality assessment is limited by executing algorithms on a single server. This is solved with the proposed placement and selection component. It allows the distribution of quality assessment tasks to mobile devices and thus increases the scalability of existing approaches by up to 33.71% when using the resources of only 15 mobile devices. These three contributions are required to provide a real-time understanding of the perceived quality of the video streams produced on mobile devices. The upload of video streams is the fourth contribution of this work. It relies on content and mechanism adaptation. The thesis introduces the first prototypically evaluated adaptive video upload protocol (LiViU) which transcodes multiple video representations in real-time and copes with changing network conditions. In addition, a mechanism adaptation is integrated into LiViU to react to changing application scenarios such as streaming high-quality videos to remote viewers or distributing video with a minimal delay to close-by recipients. A second type of content adaptation is discussed in the fifth contribution of this work. An automatic video composition application is presented which enables live composition from multiple user-generated video streams. The proposed application is the first of its kind, allowing the in-time composition of high-quality video streams by inspecting the quality of individual video streams, recording locations and cinematographic rules. As a last contribution, the content-aware adaptive distribution of video streams to mobile devices is introduced by the Video Adaptation Service (VAS). The VAS analyzes the video content streamed to understand which adaptations are most beneficial for a viewer. It maximizes the perceived quality for each video stream individually and at the same time tries to produce as little data traffic as possible - achieving data traffic reduction of more than 80%

Optimising QoE distribution for video applications through LTE-WiFi interworking

Mobile WiFi devices are becoming increasingly popular in non-seamless and user-controlled mobile traffic offloading alongside the standard WiFi hotspots. Unlike the operator-controlled hotspots, a mobile WiFi device relies on the capacity of the macro-cell for the data rate allocated to it. This type of devices can help offloading data traffic from the macro-cell base station and serve the end users within a closer range, but will change the pattern of resource distributions operated by the base station. We propose a resource allocation scheme that aims to optimize user quality of experience (QoE) when accessing video services in the environment where traffic offloading is taking place through interworking between a mobile communication system and low range wireless LANs. In this scheme, a rate redistribution algorithm is derived to perform scheduling which is controlled by a no-reference quality assessment metric in order to achieve the desired trade-offs between efficiency and fairness. We show the performance of this algorithm in terms of the distribution of the allocated data rates throughout the macro-cell investigated and the service coverage offered by the WiFi access point

Modeling Dynamics of Video Request Routing in Mobile Networks using Adaptive Scheme

3G and 4G equipment development has melodramatically increasing the mobile internet in the recent years. The devices like laptops, cellular mobiles and tablets using the mobile broadband internet like rise steeply. The most popular mobile presentation is the video streaming in the application of hypermedia. In a cost effective way, the big challenge is the quality to make obtainable these services to users. The above task is achievable by means of emerging the LTE (Long Term Evolution) in the world of mobile. With low latency and high data rates in the applications of multimedia the effective services is provided by the LTE equipment features. In this paper, we study and analyze the Quality of Experience (QoE) at the end user for Video on Demand (VoD) over the LTE network. To achieve this, we streamed High Definition (HD) videos based on H.264/AVC and these videos are delivered from foundation to destination using Transport Control Protocol (TCP) and User Datagram Protocol (UDP). Specifically, our study is about QoEassessment in terms of delay variation, packet loss metrics and provides performance assessment to characterize the impact of conveyance layer protocol in video streaming over radio systems like LTE

Tele-ultrasound imaging using smartphones and single-board PCs

BACKGROUND: Mobile devices are widely available and their computational performance increases. Nonetheless, medicine should not be an exception: single-board computers and mobile phones are crucial aides in telehealth. AIM: To explore tele-ultrasound scope using smartphones and single-board computers MATERIALS AND METHODS: This study focused on capturing ultrasound videos using external video recording devices connected via USB. Raspberry Pi single-board computers and Android smartphones have been used as platforms to host a tele-ultrasound server. Used software: VLC, Motion, and USB camera. A remote expert assessment was performed with mobile devices using the following software: VLC acted as a VLC server, Google Chrome for OS Windows 7 and OS Android was used in the remaining scenarios, and Chromium browser was installed on the Raspberry Pi computer. OUTCOMES: The UTV007 chip-based video capture device produces better images than the AMT630A-based device. The optimum video resolution was 720576 and 25 frames per second. VLC and OBS studios are considered the most suitable for a raspberry-based ultrasound system owing to low equipment and bandwidth requirements (0.640.17 Mbps for VLC; 0.5 Mbps for OBS studio). For Android phone OS, the ultrasound system was set with the USB camera software, although it required a faster network connection speed (5.20.3 Mbps). CONCLUSION: The use of devices based on single-board computers and smartphones implements a low-cost tele-ultrasound system, which potentially improves the quality of studies performed through distance learning and consulting doctors. These solutions can be used in remote regions for field medicine tasks and other possible areas of m-health

Quality Assessment of Mobile Phone Video Stabilization

Smartphone cameras are used more than ever for photography and videography. This has driven mobile phone manufacturers to develop and enhance cameras in their mobile phones. While mobile phone cameras have evolved a lot, many aspects of the mobile phone camera still have room for improvement. One is video stabilization which aims to remove unpleasant motion and artifacts from video. Many video stabilization methods for mobile phones exist. However, there is no standard video stabilization quality assessment (VSQA) framework for comparing the performance of the video stabilization methods. Huawei wanted to improve the video stabilization quality of their mobile phones by investigating video stabilization quality assessment. As a part of that endeavor, this work studies existing VSQA frameworks found in the literature and incorporates some of their ideas into a VSQA framework established in this work. The new VSQA framework consists of a repeatable laboratory environment and objective sharpness and motion metrics. To test the VSQA framework, videos were captured on multiple mobile phones in the laboratory environment. These videos were first subjectively evaluated to find issues that are noticeable by humans. Then the videos were objectively evaluated with the objective sharpness and motion metrics. The results show that the proposed VSQA framework can be used for comparing and ranking mobile devices. The VSQA framework successfully identifies the strengths and weaknesses of each tested device's video stabilization quality.Älypuhelimien kameroita käytetään nykyään valokuvaukseen enemmän kuin koskaan. Tämä on saanut älypuhelimien valmistajia kehittämään heidän puhelimiensa kameroita. Vaikka paljon edistystä on tapahtunut, niin moni älypuhelimen kameran osa-alueista kaipaa vielä kehitystä. Yksi heikoista osa-alueista on videostabilointi. Videostabiloinnin tarkoitus on poistaa videosta epämiellyttävä liike. Monia ratkaisuja löytyy, mutta mitään standardoitua tapaa vertailla eri stabilointi ratkaisuja ei ole. Huawei haluaa parantaa tuotteidensa videostabiloinnin laatua. Saavuttaakseen tämän tavoitteen, tässä työssä tehdään katsaus kirjallisuudesta löytyviä videostabiloinnin laadun mittausmenetelmiä ja jalostetaan näistä ideoita, joiden avulla kehitetään oma videonstabiloinnin laadun mittausmenetelmä. Menetelmä koostuu toistettavasta laboratorioympäristöstä, jossa voi kuvata heiluvia videoita eri älypuhelimilla. Näitä videoita vertaillaan objektiivisesti mittaamalla videoista terävyyttä ja liikkeen miellyttävyyttä. Työn videostabiloinnin laadun mittausmenetelmää testattiin kuvaamalla toistettavassa laboratorioympäristössä usealla älypuhelimella videoita, joissa on simuloitua käden tärinää. Ensin kuvattuja videoita arvioitiin ja vertailtiin subjektiivisesti, jotta niistä löytyisi ongelmat, joita videostabilointi ei ole onnistunut korjaamaan. Tämän jälkeen videoita arvioitiin objektiivisilla terävyys- ja liikemittareilla. Tulokset osoittavat, että työssä esitetty videostabiloinnin laadun mittausmenetelmää voidaan käyttää eri älypuhelimien videostabilointimenetelmien vertailuun. Työn mittausmenetelmä onnistui havaitsemaan eri video stabilointimenetelmien vahvuudet ja heikkoudet

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

Energy-aware adaptive solutions for multimedia delivery to wireless devices

The functionality of smart mobile devices is improving rapidly but these devices are limited in terms of practical use because of battery-life. This situation cannot be remedied by simply installing batteries with higher capacities in the devices. There are strict limitations in the design of a smartphone, in terms of physical space, that prohibit this “quick-fix” from being possible. The solution instead lies with the creation of an intelligent, dynamic mechanism for utilizing the hardware components on a device in an energy-efficient manner, while also maintaining the Quality of Service (QoS) requirements of the applications running on the device. This thesis proposes the following Energy-aware Adaptive Solutions (EASE): 1. BaSe-AMy: the Battery and Stream-aware Adaptive Multimedia Delivery (BaSe-AMy) algorithm assesses battery-life, network characteristics, video-stream properties and device hardware information, in order to dynamically reduce the power consumption of the device while streaming video. The algorithm computes the most efficient strategy for altering the characteristics of the stream, the playback of the video, and the hardware utilization of the device, dynamically, while meeting application’s QoS requirements. 2. PowerHop: an algorithm which assesses network conditions, device power consumption, neighboring node devices and QoS requirements to decide whether to adapt the transmission power or the number of hops that a device uses for communication. PowerHop’s ability to dynamically reduce the transmission power of the device’s Wireless Network Interface Card (WNIC) provides scope for reducing the power consumption of the device. In this case shorter transmission distances with multiple hops can be utilized to maintain network range. 3. A comprehensive survey of adaptive energy optimizations in multimedia-centric wireless devices is also provided. Additional contributions: 1. A custom video comparison tool was developed to facilitate objective assessment of streamed videos. 2. A new solution for high-accuracy mobile power logging was designed and implemented

The Big Picture on Small Screens Delivering Acceptable Video Quality in Mobile TV

Mobile TV viewers can change the viewing distance and (on some devices) scale the picture to their preferred viewing ratio, trading off size for angular resolution. We investigated optimal trade-offs between size and resolution through a series of studies. Participants selected their preferred size and rated the acceptability of the visual experience on a 200ppi device at a 4: 3 aspect ratio. They preferred viewing ratios similar to living room TV setups regardless of the much lower resolution: at a minimum 14 pixels per degree. While traveling on trains people required videos with a height larger than 35mm