Survey on QoE/QoS Correlation Models for Video Streaming over Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANETs) are a new emerging technology which has attracted enormous interest over the last few years. It enables vehicles to communicate with each other and with roadside infrastructures for many applications. One of the promising applications is multimedia services for traffic safety or infotainment. The video service requires a good quality to satisfy the end-user known as the Quality of Experience (QoE). Several models have been suggested in the literature to measure or predict this metric. In this paper, we present an overview of interesting researches, which propose QoE models for video streaming over VANETs. The limits and deficiencies of these models are identified, which shed light on the challenges and real problems to overcome in the future

Architecture and Protocol to Optimize Videoconference in Wireless Networks

[EN] In the past years, videoconferencing (VC) has become an essential means of communications. VC allows people to communicate face to face regardless of their location, and it can be used for different purposes such as business meetings, medical assistance, commercial meetings, and military operations. There are a lot of factors in real-time video transmission that can affect to the quality of service (QoS) and the quality of experience (QoE). The application that is used (Adobe Connect, Cisco Webex, and Skype), the internet connection, or the network used for the communication can affect to the QoE. Users want communication to be as good as possible in terms of QoE. In this paper, we propose an architecture for videoconferencing that provides better quality of experience than other existing applications such as Adobe Connect, Cisco Webex, and Skype. We will test how these three applications work in terms of bandwidth, packets per second, and delay using WiFi and 3G/4G connections. Finally, these applications are compared to our prototype in the same scenarios as they were tested, and also in an SDN, in order to improve the advantages of the prototype.This work has been supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Jimenez, JM.; García-Navas, JL.; Lloret, J.; Romero Martínez, JO. (2020). Architecture and Protocol to Optimize Videoconference in Wireless Networks. Wireless Communications and Mobile Computing. 2020:1-22. https://doi.org/10.1155/2020/4903420S122202

Quantifying subjective quality evaluations for mobile video watching in a semi-living lab context

This paper discusses results from an exploratory study in which Quality of Experience aspects related to mobile video watching were investigated in a semi-living lab setting. More specifically, we zoom in on usage patterns in a natural research context and on the subjective evaluation of high and low-resolution movie trailers that are transferred to a mobile device using two transmission protocols for video (i.e., real-time transport protocol and progressive download using HTTP). User feedback was collected by means of short questionnaires on the mobile device, combined with traditional pen and paper diaries. The subjective evaluations regarding the general technical quality, perceived distortion, fluentness of the video, and loading speed are studied and the influence of the transmission protocol and video resolution on these evaluations is analyzed. Multinomial logistic regression results in a model to estimate the subjective evaluations regarding the perceived distortion and loading speed based on objectively-measured parameters of the video session

Network Performance Criteria for Telecommunication Traffic Types driven by Quality of Experience

A common reason for changing the chosen service provider is the users\u27 perception of service. Quality of Experience (QoE) describes the end user\u27s perception of service while using it. A frequent cause of QoE degradation is inadequate traffic routing, where, other than throughput, selected routes do not satisfy minimum network requirements for the given service or services. In order to enable QoE-driven routing, per traffic type defined routing criteria are required. Our goal was to obtain those criteria for relevant services of a telecom operator. For the purpose of identifying services of interest, we first provide short results of user traffic analysis within the telecom operator network. Next, our work presents testbed measurements which explore the impact of packet loss and delay on user QoE for video, voice, and management traffic. For video services, we investigated separately multicast delivery, unicast HTTP Live Streaming (HLS), and unicast Real Time Streaming Protocol (RTSP) traffic. Applying a threshold to QoE values, from the measured dependencies we extracted minimum network performance criteria for the investigated different types of traffic. Finally, we provide a comparison with results available in the literature on the topic

Smart resource allocation for improving QoE in IP Multimedia Subsystems

[EN] IP Multimedia Subsystem (IMS) is a robust multimedia service. IMS becomes more important when delivering multimedia services. Multimedia service providers can benefit from IMS to ensure a good QoE (Quality of Experience) to their customers with minimal resources usage. In this paper, we propose an intelligent media distribution IMS system architecture for delivering video streaming. The system is based primarily on uploading a multimedia file to a server in the IMS. Later, other users can download the uploaded multimedia file from the IMS. In the system, we also provide the design of the heuristic decision methods and models based on probability distributions. Thus, our system takes into account the network parameters such as bandwidth, jitter, delay and packet loss that influence the QoE of the end -users. Moreover, we have considered the other parameters of the energy consumption such as CPU, RAM, temperature and number connected users that impact the result of the QoE. All these parameters are considered as input to our proposal management system. The measurements taken from the real test bench show the real performance and demonstrate the success of the system about ensuring the upload speed of the multimedia file, guaranteeing the QoE of end users and improving the energy efficiency of the IMS.This work has been partially supported by the "Ministerio de Ciencia e Innovation", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigation Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, though the PAID-15-11 multidisciplinary projects.Canovas Solbes, A.; Taha, M.; Lloret, J.; Tomás Gironés, J. (2018). Smart resource allocation for improving QoE in IP Multimedia Subsystems. Journal of Network and Computer Applications. 104:107-116. https://doi.org/10.1016/j.jnca.2017.12.020S10711610

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

Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction