Quality perception when streaming video on tablet devices

Image Quality and System Performance XI, 2014

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients' behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

2013

This paper deals with the video quality evaluation of mobile video delivery. It analyzes the current trends in video quality evaluation while trying to shift video quality measurement from service-centered to more user-centered. The attention is in defining the causalities among specific video and network parameters, and perceived video quality. As a part of this target, vast subjective video quality survey has been conducted to define these causalities. These causalities were particularly important in designing of our video quality prediction model based on Bayesian network. Proposed solution and prediction results were then experimentally evaluated compared to video quality results of independent subjective video quality assessment.

Proceedings of the 7th …, 2009

Measuring and predicting the user's Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the user's feedback. Existing statistical techniques have lead to poor accuracy (order of 70%) and ...

Quality-of-Experience (QoE) is a human centric notion that produces the blue print of human perception, feelings, needs and intentions while Quality-of-Service (QoS) is a technology centric metric used to assess the performance of a multimedia application and/or network. To ensure superior video QoE, it is important to understand the relationship between QoE and QoS. To achieve this goal, we conducted a pilot subjective user study simulating a video streaming service over a broadband network with varying distortion scenarios, namely packet losses (0, 0.5, 1, 3,7, and 15%), packet reorder (0, 1, 5, 10, 20, and 30%), and coding bit rates (100, 400, 600, and 800 Kbps). Users were asked to rate their experience using a subjective quantitative metric (termed Perceived Video Quality, PVQ) and qualitative indicators of “experience.” Simulation results suggest a) an exponential relationship between PVQ and packet loss and between PVQ and packet reorder, and b) a logarithmic relationship between PVQ and video bit rate. Similar trends were observed with the qualitative indicators. Exploratory analysis with two objective video quality metrics suggests that trends similar to those obtained with the subjective ratings were obtained, particularly with a full-reference metric.

2014 IEEE International Conference on Consumer Electronics (ICCE), 2014

In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users' subjectivity must also be considered. In this context, a video quality assessment method that takes into account the user's preference for video content is proposed in this research. In order to perform this task, the users' profiles that include their preferences were stored in a video server. Then, subjective tests of video quality assessment were conducted, in which evaluators had different video content preferences. Results show that the evaluators' QoE is highly correlated with the user's preference for video content type. Based on these experimental results, a function named Preference Factor (PF) is defined and used to adjust the quality index values obtained by an objective video quality metric running in the end user's device. The PF function also depends on video content type and quality index score. Using the PF function, the enhanced Video streaming Quality Metric (e-VsQM) is proposed and the results of its performance evaluation demonstrate that PF improves an objective video quality metric. Furthermore, e-VsQM has low complexity and can be utilized in different video services. Thus, an application scenario is presented, in which the proposed video quality metric is implemented 1 .

2011

Concerning video transmission on the Internet, we present a model for estimating the subjective quality from objective measurements at the transmission receivers and on the network. The model reflects the quality degradation subject to parameters like packet loss ratio and bit rate, and is calibrated using the prerecorded results from subjective quality assessments. Besides the model and the calibration, the main achievement of this paper is the model's validation by implementation in a monitoring tool. It can be used by content and network providers to swiftly localise the causes of a poor quality of experience (QoE). It also can help content providers make decisions regarding the adjustment of vital parameters, such as encoding bit rate and error correction mechanisms. We show how the estimated subjective service quality can be applied for decision making in content delivery networks that consist of overlay networks and multi-access networks.

2011

HTTP video streaming, such as Flash video, is widely deployed to deliver stored media. Owing to TCP's reliable service, the picture and sound quality would not be degraded by network impairments, such as high delay and packet loss. However, the network impairments can cause rebuffering events which would result in jerky playback and deform the video's temporal structure. These quality degradations could adversely affect users' quality of experience (QoE). In this paper, we investigate the relationship among three levels of quality of service (QoS) of HTTP video streaming: network QoS, application QoS, and user QoS (i.e., QoE). Our ultimate goal is to understand how the network QoS affects the QoE of HTTP video streaming. Our approach is to first characterize the correlation between the application and network QoS using analytical models and empirical evaluation. The second step is to perform subjective experiments to evaluate the relationship between application QoS and QoE. Our analysis reveals that the frequency of rebuffering is the main factor responsible for the variations in the QoE.

2014

This paper presents the results of the subjective evaluation of Quality of Experience (QoE) for video streaming service. The evaluation was conducted among 602 test subjects who rated the quality of 72 videos by watching them at their homes. The quality of each video was uniquely distorted (packet loss rate, number of packet loss occurrences during video streaming and total duration of those occurrences varied in each video). The results revealed that, in real-life environment, test subjects failed to notice significant number of video segments whose quality was degraded, they could not accurately quantify their total duration and they usually did not maintain negative attitude about perceived quality distortions.

Advances in Human- …

Managing multimedia network services in a Usercentric manner provides for more delivered quality to the users, whilst maintaining a limited footprint on the network resources. For efficient User-centric management it is imperative to have a precise metric for perceived quality. Quality of Experience (QoE) is such a metric, which captures many different aspects that compose the perception of quality. The drawback of using QoE is that due to its subjectiveness, accurate measurement necessitates execution of cumbersome subjective studies. In this work we propose a method that uses Machine Learning techniques to build QoE prediction models based on limited subjective data. Using those models we have developed an algorithm that generates the remedies for improving the QoE of observed multimedia stream. Selecting the optimal remedy is done by comparing the costs in resources associated to each of them. Coupling the QoE estimation and calculation of remedies produces a tool for effective implementation of a User-centric management loop for multimedia streaming services.

2017

Studies have shown that video streaming is one of prominent cellular applications. Apart from improved network capacity, its growth is also catalysed by usage of smart-devices. It is therefore important for service providers to understand how key characteristics of smart devices, network and video simultaneously influence users’ Quality of Experience (QoE). This paper presents a mapping function which was derived after a series of subjective experiments to study users’ responses on video streaming QoE conducted over a wireless test-bed. It achieved a good prediction accuracy of different video types streamed in a wireless environment. Keywords—QoE, QoS, video streaming; mapping function