Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.

A novel methodology for QoE evaluation in the social network environment is proposed. It provides high applicability for subjective testing of the multimedia services with respect to real usage scenarios. The environment of social networks provides also significant demographic data and ability to contact extremely many test subjects while allows to focus on or filter specific social groups. QoE results for HD internet video services are presented and followed by discussion on their statistical significance.

Provisioning of mobile video services is rather challenging since in mobile environments, bandwidth and processing resources are limited. Audiovisual content is present in most multimedia services, however, the user expectation of perceived audiovisual quality differs for speech and non-speech contents. The majority of recently proposed metrics for audiovisual quality estimation assumes only one continuous medium, either audio or video. In order to accurately predict the audiovisual quality of a multi-media system it is necessary to apply a metric that takes simultaneously into account audio as well as video quality. When assessing a multi-modal system, one cannot model it only as a simple combination of mono-modal models, because the pure combination of audio and video models does not give a robust perceived-quality performance metric. We show the importance taking into account the cross-modal interaction between audio and video modes also code mutual compensation effect. In this contribution we report on measuring the cross-modal interaction and propose a content adaptive audiovisual metric for video sequences that distinguishes between speech and non-speech audio. Furthermore, the proposed method allows for a reference-free audiovisual quality estimation, which reduces computational complexity and extends applicability.

The scope of this work is the estimation of video quality for low resolution video sequences typical in (mobile) video streaming applications. Since the video quality experienced by users depends considerably on the spatial (edges, colors, ...) and temporal (movement speed, direction, ...) features of the video sequence, this paper presents a two-step approach to quality estimation. Firstly, shots between two scene changes are analyzed and their content class is found. Secondly, based on the content class, frame rate and bitrate, an estimation of quality is carried out. In this paper, the design of the content classifier as well as an appropriate choice of the content classes and their characteristics is discussed. Moreover, the design of quality metric is presented, based on the mean opinion score obtained by a survey. The performance of the proposed method is evaluated and compared to several common methods. The results show that the proposed approach provides powerful means of estimating the video quality experienced by users for low resolution video streaming services.