A survey on usage of mobile video in Australia

The growth of powerful entertainment functions of mobile devices, in particular mobile video, has recently attracted much attention. Studies on mobile TV, one form of mobile video, have been conducted in many countries. However, little research focuses on the holistic usage of mobile video. To understand the features of such usage, we conducted an online survey in Brisbane, Australia, during the first half of 2010. Our findings reveal similarities and diversities between usage of mobile TV in particular and mobile video on the whole. The results could aid in improving the design of future studies, with a view to ultimately increase user satisfaction.

Quality delivery of mobile video : in-depth understanding of user requirements

The increase of powerful mobile devices has accelerated the demand for mobile videos. Previous studies in mobile video have focused on understanding of mobile video usage, improvement of video quality, and user interface design in video browsing. However, research focusing on a deep understanding of users’ needs for a pleasing quality delivery of mobile video is lacking. In particular, what quality-delivery mode users prefer and what information relevant to video quality they need requires attention. This paper presents a qualitative interview study with 38 participants to gain an insight into three aspects: influencing factors of user-desired video quality, user-preferred quality-delivery modes, and user-required interaction information of mobile video. The results show that user requirements for video quality are related to personal preference, technology background and video viewing experience, and the preferred quality-delivery mode and interactive mode are diverse. These complex user requirements call for flexible and personalised quality delivery and interaction of mobile video.

Saving bitrate vs. pleasing users : where is the break-even point in mobile video quality?

This paper presents a comprehensive study to find the most efficient bitrate requirement to deliver mobile video that optimizes bandwidth, while at the same time maintains good user viewing experience. In the study, forty participants were asked to choose the lowest quality video that would still provide for a comfortable and long-term viewing experience, knowing that higher video quality is more expensive and bandwidth intensive. This paper proposes the lowest pleasing bitrates and corresponding encoding parameters for five different content types: cartoon, movie, music, news and sports. It also explores how the lowest pleasing quality is influenced by content type, image resolution, bitrate, and user gender, prior viewing experience, and preference. In addition, it analyzes the trajectory of users’ progression while selecting the lowest pleasing quality. The findings reveal that the lowest bitrate requirement for a pleasing viewing experience is much higher than that of the lowest acceptable quality. Users’ criteria for the lowest pleasing video quality are related to the video’s content features, as well as its usage purpose and the user’s personal preferences. These findings can provide video providers guidance on what quality they should offer to please mobile users.

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.

Impact of region-of-interest video coding on perceived quality in mobile video

Effective streaming of video can be achieved by providing more bits to the most important region in the frame at the cost of reduced bits in the less important regions. This strategy can be beneficial for delivering high quality videos in mobile devices, especially when the availability of bandwidth is usually low and limited. While the state-of-the-art video codecs such as H.264 may have been optimised for perceived quality, it is hypothesised that users will give more attention to interesting region/object when watching videos. Therefore, giving a higher quality to region of interest (ROI)while reducing quality of other areas may result in improving the overall perceived quality without necessarily increasing the bitrate. In this paper, the impact of ROI-based encoded video on perceived quality is investigated by conducting a user study for varous target bitrates. The results from the user study demonstrate that ROI-based video coding has superior perceived quality compared to normal encoded video at the same bitrate in the lower bitrate range.

User-driven quality of experience modelling for mobile video optimisation

The increasing demand for mobile video has attracted much attention from both industry and researchers. To satisfy users and to facilitate the usage of mobile video, providing optimal quality to the users is necessary. As a result, quality of experience (QoE) becomes an important focus in measuring the overall quality perceived by the end-users, from the aspects of both objective system performance and subjective experience. However, due to the complexity of user experience and diversity of resources (such as videos, networks and mobile devices), it is still challenging to develop QoE models for mobile video that can represent how user-perceived value varies with changing conditions. Previous QoE modelling research has two main limitations: aspects influencing QoE are insufficiently considered; and acceptability as the user value is seldom studied. Focusing on the QoE modelling issues, two aims are defined in this thesis: (i) investigating the key influencing factors of mobile video QoE; and (ii) establishing QoE prediction models based on the relationships between user acceptability and the influencing factors, in order to help provide optimal mobile video quality. To achieve the first goal, a comprehensive user study was conducted. It investigated the main impacts on user acceptance: video encoding parameters such as quantization parameter, spatial resolution, frame rate, and encoding bitrate; video content type; mobile device display resolution; and user profiles including gender, preference for video content, and prior viewing experience. Results from both quantitative and qualitative analysis revealed the significance of these factors, as well as how and why they influenced user acceptance of mobile video quality. Based on the results of the user study, statistical techniques were used to generate a set of QoE models that predict the subjective acceptability of mobile video quality by using a group of the measurable influencing factors, including encoding parameters and bitrate, content type, and mobile device display resolution. Applying the proposed QoE models into a mobile video delivery system, optimal decisions can be made for determining proper video coding parameters and for delivering most suitable quality to users. This would lead to consistent user experience on different mobile video content and efficient resource allocation. The findings in this research enhance the understanding of user experience in the field of mobile video, which will benefit mobile video design and research. This thesis presents a way of modelling QoE by emphasising user acceptability of mobile video quality, which provides a strong connection between technical parameters and user-desired quality. Managing QoE based on acceptability promises the potential for adapting to the resource limitations and achieving an optimal QoE in the provision of mobile video content.

User-centred study on quality of mobile video services

Mobile video, as an emerging market and a promising research field, has attracted much attention from both industry and researchers. Considering the quality of user-experience as the crux of mobile video services, this chapter aims to provide a guide to user-centered studies of mobile video quality. This will benefit future research in better understanding user needs and experiences, designing effective research, and providing solid solutions to improve the quality of mobile video. This chapter is organized in three main parts: (1) a review of recent user studies from the perspectives of research focuses, user study methods, and data analysis methods; (2) an example of conducting a user study of mobile video research, together with the discussion on a series of relative issues, such as participants, materials and devices, study procedure, and analysis results, and; (3) a conclusion with an open discussion about challenges and opportunities in mobile video related research, and associated potential future improvements.

Acceptability-based QoE models for mobile video

Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE computing models have two main limitations: 1) insufficient consideration of the factors influencing QoE, and; 2) limited studies on QoE models for acceptability prediction. In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users’ acceptability and pleasantness in various mobile video usage scenarios. Statistical regression analysis has been used to build the models with a group of influencing factors as independent predictors, including encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery decisions.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming

Impact of automatic region-of-interest coding on perceived quality in mobile video

At present, the most reliable method to obtain end-user perceived quality is through subjective tests. In this paper, the impact of automatic region-of-interest (ROI) coding on perceived quality of mobile video is investigated. The evidence, which is based on perceptual comparison analysis, shows that the coding strategy improves perceptual quality. This is particularly true in low bit rate situations. The ROI detection method used in this paper is based on two approaches: - (1) automatic ROI by analyzing the visual contents automatically, and; - (2) eye-tracking based ROI by aggregating eye-tracking data across many users, used to both evaluate the accuracy of automatic ROI detection and the subjective quality of automatic ROI encoded video. The perceptual comparison analysis is based on subjective assessments with 54 participants, across different content types, screen resolutions, and target bit rates while comparing the two ROI detection methods. The results from the user study demonstrate that ROI-based video encoding has higher perceived quality compared to normal video encoded at a similar bit rate, particularly in the lower bit rate range.

Dealing with Energy-QoE Trade-Offs in Mobile Video

Scalable video coding allows an efficient provision of video services at different quality levels with different energy demands. According to the specific type of service and network scenario, end users and/or operators may decide to choose among different energy versus quality combinations. In order to deal with the resulting trade-off, in this paper we analyze the number of video layers that are worth to be received taking into account the energy constraints. A single-objective optimization is proposed based on dynamically selecting the number of layers, which is able to minimize the energy consumption with the constraint of a minimal quality threshold to be reached. However, this approach cannot reflect the fact that the same increment of energy consumption may result in different increments of visual quality. Thus, a multiobjective optimization is proposed and a utility function is defined in order to weight the energy consumption and the visual quality criteria. Finally, since the optimization solving mechanism is computationally expensive to be implemented in mobile devices, a heuristic algorithm is proposed. This way, significant energy consumption reduction will be achieved while keeping reasonable quality levels.

Exploration and optimization of user experience in viewing videos on a mobile phone

Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user’s viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user’s acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user’s assessment criteria and to support the quantitative results from a user’s perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user’s opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.

Mobile multimedia : user and technology perspectives

As multimedia-enabled mobile devices such as smart phones and tablets are becoming the day-to-day computing device of choice for users of all ages, everyone expects that all mobile multimedia applications and services should be as smooth and as high-quality as the desktop experience. The grand challenge in delivering multimedia to mobile devices using the Internet is to ensure the quality of experience that meets the users' expectations, within reasonable costs, while supporting heterogeneous platforms and wireless network conditions. This book aims to provide a holistic overview of the current and future technologies used for delivering high-quality mobile multimedia applications, while focusing on user experience as the key requirement. The book opens with a section dealing with the challenges in mobile video delivery as one of the most bandwidth-intensive media that requires smooth streaming and a user-centric strategy to ensure quality of experience. The second section addresses this challenge by introducing some important concepts for future mobile multimedia coding and the network technologies to deliver quality services. The last section combines the user and technology perspectives by demonstrating how user experience can be measured using case studies on urban community interfaces and Internet telephones.

QoE modelling for VP9 and H.265 videos on mobile devices

Current mobile devices and streaming video services support high definition (HD) video, increasing expectation for more contents. HD video streaming generally requires large bandwidth, exerting pressures on existing networks. New generation of video compression codecs, such as VP9 and H.265/HEVC, are expected to be more effective for reducing bandwidth. Existing studies to measure the impact of its compression on users’ perceived quality have not been focused on mobile devices. Here we propose new Quality of Experience (QoE) models that consider both subjective and objective assessments of mobile video quality. We introduce novel predictors, such as the correlations between video resolution and size of coding unit, and achieve a high goodness-of-fit to the collected subjective assessment data (adjusted R-square >83%). The performance analysis shows that H.265 can potentially achieve 44% to 59% bit rate saving compared to H.264/AVC, slightly better than VP9 at 33% to 53%, depending on video content and resolution.