Mobile Video Data Research Articles

Participating researcher or researching participant?

The article explores different participant positions that are available to researchers of social interaction during the collection of mobile video data. In the data presented, participants are engaged in outdoor activities that essentially involve some form and amount of mobility. The authors analyse the positions they have adopted in collecting data involving groups of mobile participants. The positions have varied depending on whether the activities allow, or even assume, researchers to draw on some specific participant knowledge. The article focuses on moments of adjustment during which the authors, as researchers collecting data, evidently make decisions about what to record and how to participate in the ongoing activity, and which thus reflect their spontaneous, negotiable and planned participation on site. As researchers of social interaction increasingly draw on data that involve mobility, it is pertinent to consider the possible positions that they may adopt and the practices that they employ in the collection and analysis of such data.

A Utility-Based Framework for Performance and Energy-Aware Convergence in 5G Heterogeneous Network Environments

The integration of a broadcast oriented RAN architecture into a convergent framework for heterogeneous networks offers two major contributions. On the one hand, it presents an interesting solution to satisfy the exponential growth of the mobile video data traffic, and on the other hand, it is also identified as a promising key technology, that can contribute to improve the energy efficiency of 5G networks. This work proposes a novel Convergent Architecture for Broadcast, Broadband and Cellular services (CABS). This architecture is complemented with the Performance and Energy-aware Access (PEC) network selection algorithm. This joint solution offers a balanced trade-off between the user perceived QoS and the network energy efficiency in challenging heterogeneous scenarios. Furthermore, this approach guarantees the broadcast service continuity in harsh environments, and therefore, is helpful in one of the most critical scenarios for broadcasting industry: mobile and indoor reception. The proposed solution was modelled and tested in Network Simulator version 3 (NS-3), in which among other updates, a first version of an ATSC 3.0 model was introduced.

Video encoding adaptation for QoE maximization over 5G cellular networks

With the unprecedented growth of mobile video data traffic every year, increasing users' video quality of experience (QoE) under limited network radio resources becomes a critical issue in next-generation cellular systems. However, videos compressed with unsuitable video encoding parameters will waste the network radio resources and users even may not be able to get satisfied video QoE. Specifically, a video with higher bit rate requirement (determined by the video encoding parameters) does not imply that a user will have better QoE. This paper studies the QoE optimization problem via carefully determining a combination of video encoding parameters for each user under the radio resource block constraint. The objective is to maximize the total QoE of all users. We prove that our target problem is NP-hard and propose an algorithm based on dynamic programming to solve the problem. Then, we prove that our proposed algorithm is a pseudo-polynomial time optimal algorithm. We construct a series of simulations with realistic video sequences encoded by H.264 and network settings to evaluate the performance of our proposed algorithm. Compared with two baselines, the simulation results show that the proposed algorithm can significantly improve the total QoE of all users and indicate that the video encoding adaptation is an important issue in the QoE optimization problem.

Video interaction: a research agenda

The topic of this special issue is the broadened use of video and the need to articulate a research agenda that addresses its new opportunities and challenges. This agenda, which we argue should be labelled ‘‘video interaction,’’ is influenced by both emerging practices and technical developments. In the widest sense, we refer to video interaction as a research area concerned with emerging technologies and social practices in an increasingly flattened hierarchy between, on the one hand, what used to be a well-defined group of production professionals, and on the other hand, the masses of passive viewers of the same media. This transition happens at the same time as video traffic has become the bulk of data communication on the Internet [2]. The same shift is now happening in mobile data. Cisco reports that mobile video traffic exceeded 50 % for the first time in 2012. They predict that the numbers for video will continue to increase dramatically. In 5 years, the amount of mobile video data is projected to increase 16-fold, ending up at over two-thirds of the entire data traffic [12]. Obviously, downloading and streaming of movies and TV series are the big drivers in this development, but there are a number of other things happening that warrant a new approach to understanding interaction with video content, beyond the notion of the user as a viewer in the traditional sense. The practices surrounding moving image technology are now transcending consumption of traditional media, and becoming integrated with other interactive services and social media [2]. In this massive growth of video online, there are a number of parallel trends regarding how video is consumed, produced, shared, and interacted with that we argue require a re-conceptualisation of the area. In the following, we will map out the changed use of video and then analyze its opportunities and challenges, and explain the need for a coherent research approach to video interaction. In particular, the trends we want to align are a shift to user-generated video content and to mobile technology, as well as a continuation of the trend of increased interaction in viewing (iTV). First, new ways of producing video content has emerged, enabled by the availability of cheap production tools and high bandwidth communication networks. It emerged with the use of both analogue and digital video cameras for consumers, and continued with video recording facilities on mobile phones [30]. The cost and effort of sharing and distributing the content have also diminished with the expansion of the fixed Internet. Live video production is now on the verge of the same kind of democratization of means of production and broader adoption by nonprofessional users. Through highly visible examples such as live broadcasts from the Arabic Spring and other news events around the world, as well as more mundane practices of broadcasting from university lectures and local events, live video has shown potential for communicating and sharing experiences with remote viewers. Second, we see an expansion of services that utilize mobile technology in a broad sense, and that are catering to O. Juhlin G. Zoric (&) A. Engstrom Stockholm, Sweden e-mail: goga@mobilelifecentre.org

Use of Mobile Video Data Collection Equipment to Investigate Winter Weather Vehicle Speeds

Mobile video data collection equipment was used to collect data and determine traffic flow and roadway surface conditions during seven winter weather events. Driver visibility was estimated manually. The use of the mobile video data collection equipment in this research and some concerns related to its use during winter weather are discussed. The characteristics of the data are summarized, and the results of a multiple regression statistical analysis presented. Winter weather data were collected and summarized by 15-min time periods. The data included traffic volumes, vehicle speeds, vehicle gaps or headways, visibility, and roadway snow cover. The average winter weather vehicle speed was significantly lower than normal, and multiple regression indicated a relationship among the average off-peak winter weather vehicle speed, the square of hourly volume, visibility, and roadway snow cover. The traffic volume variable in this model was believed to be a surrogate for weather data not collected. The model predicts that visibility below 0.4 km (0.25 mi) and snow cover on the roadway lanes may reduce average off-peak winter weather vehicle speeds by approximately 6.3 and 11.7 km/h (3.9 and 7.3 mph), respectively. It was concluded that the collection of traffic flow and roadway surface data during winter weather events requires the seamless integration, planning, and calibration of a partially video-based automated permanent or semipermanent information system. The ability to assess, quantify, and communicate the potential vehicle speed effects of winter weather could assist drivers with travel decisions and public-sector workers with maintenance decisions.