This paper proposes a subjective model for estimating the quality of video streaming services with dynamic bit-rate control. In a subjective quality assessment test, we clarify users' perceptions of distributed video signals whose quality is time-variant due to dynamic bit-rate control. Using this result, we constructed an estimation model considering the following three characteristics: 1) the influence of the video section where quality degradation is large will strongly affect the overall quality, 2) the impression of a past quality weakens with the passage of time, and 3) the range of evaluation scores becomes wider when the time duration of an evaluation is longer. We found that the proposed model enables the accuracy of estimating overall subjective quality to be dramatically improved compared with that of a model that averages segmental quality. The estimation error of the proposed model is less than the statistical reliability of the subjective score even for verification data. We also show that our findings are applicable to QoS design/management issues for video streaming services with dynamic bit-rate control.

Many subjective assessment methods for video quality are provided by ITU-T and ITU-R recommendations, but the differences among these methods have not been sufficiently studied. We compare five subjective assessment methods using four quantitative performance indices for both HD and QVGA resolution video. We compare the Double-Stimulus Continuous Quality-Scale (DSCQS), Double-Stimulus Impairment Scale (DSIS), Absolute Category Rating method (ACR), and ACR with Hidden Reference (ACR-HR) as common subjective assessment methods for HD and QVGA resolution videos. Furthermore, we added ACR with an 11-grade scale (ACR11) for the HD test and Subjective Assessment of Multimedia Video Quality (SAMVIQ) for the QVGA test for quality scale variations. The performance indices are correlation coefficients, rank correlation coefficients, statistical reliability, and assessment time. For statistical reliability, we propose a performance index for comparing different quality scale tests. The results of the performance comparison showed that the correlation coefficients and rank correlation coefficients of the mean opinion scores between pairs of methods were high for both HD and QVGA tests. As for statistical reliability provided by the proposed index, DSIS of HD and ACR of QVGA outperformed the other methods. Moreover, ACR, ACR-HR, and ACR11 were the most efficient subjective quality assessment methods from the viewpoint of assessment time.

Service providers should monitor the quality of experience of a communication service in real time to confirm its status. To do this, we previously proposed a packet-layer model that can be used for monitoring the average video quality of typical Internet protocol television content using parameters derived from transmitted packet headers. However, it is difficult to monitor the video quality per user using the average video quality because video quality depends on the video content. To accurately monitor the video quality per user, a model that can be used for estimating the video quality per video content rather than the average video quality should be developed. Therefore, to take into account the impact of video content on video quality, we propose a model that calculates the difference in video quality between the video quality of the estimation-target video and the average video quality estimated using a packet-layer model. We first conducted extensive subjective quality assessments for different codecs and video sequences. We then model their characteristics based on parameters related to compression and packet loss. Finally, we verify the performance of the proposed model by applying it to unknown data sets different from the training data sets used for developing the model.

In this paper, we consider network monitoring techniques to estimate communication qualities in wide-area mobile networks, where an enormous number of heterogeneous components such as base stations, routers, and servers are deployed. We assume that average delays of neighboring base stations are comparable, most of servers have small delays, and delays at core routers are negligible. Under these assumptions, we propose Heterogeneous Delay Tomography (HDT) to estimate the average delay at each network component from end-to-end round trip times (RTTs) between mobile terminals and servers. HDT employs a crowdsourcing approach to collecting RTTs, where voluntary mobile users report their empirical RTTs to a data collection center. From the collected RTTs, HDT estimates average delays at base stations in the Graph Fourier Transform (GFT) domain and average delays at servers, by means of Compressed Sensing (CS). In the crowdsourcing approach, the performance of HDT may be degraded when the voluntary mobile users are unevenly distributed. To resolve this problem, we further extend HDT by considering the number of voluntary mobile users. With simulation experiments, we evaluate the performance of HDT.