Video Adaptation for Transmission Channels by Utility Modeling

Tanzania Journal of Science, 2021

Maintaining quality of streaming video is challenged by network faults resulting into freezes and rebufferings on the video. On top of the network effects, device features have impacts on the image of the video frames displayed during streaming. Despite the simultaneous impacts of video quality from network and device, previous studies considered individual impact of network parameters or devices as influencing factors to propose Quality of Experience (QoE) models. This study proposed QoE model by mapping combined effects from both network and device on video streamed QoE. An experiment to study the effects of video quality from combined effects of network and device over the wireless involved 35 subjects. Combination of packet loss, packet reordering, and delay were emulated using network emulator following Design of Experiment methodology. Through analysis of variance, the study found that packet loss had the highest impact, followed by device features, reordering, and delay on the video QoE. From the combined effects, two-way interactions and three-way interactions had significant effects on the video QoE. Through additive and linearity behavior of the input factors from network and device on video streaming QoE, a multi-factor model was derived.

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.

An approach t o the design of user-centred communications protocols is presented. Quality of Perception (QoP) is a novel term which includes not only users' enjoyment and satisfaction with a multimedia presentation, but also their ability to assimilate its informational content. We show that a relation of proportionality can be obtained between QoP and low-level Quality of Service (QoS) parameters if t h e multimedia presentation content is taken into account. This relation could provide t h e basis for a preferential QoS parameter management scheme which is also user-oriented.

International Journal of Human-Computer Studies, 2006

In general, video quality adaptation and video quality evaluation are distinct activities. Most adaptive delivery mechanisms for streaming multimedia content do not explicitly consider user-perceived quality when making adaptation decisions. Equally, video quality evaluation techniques are not designed to evaluate instantaneous quality where the quality is changing over time. We propose that an Optimal Adaptation Trajectory (OAT) through the set of possible encoding exists, and that it indicates how to adapt encoding quality in response to changes in network conditions in order to maximize user-perceived quality. The subjective and objective tests carried out to find such trajectories for a number of different MPEG-4 video clips are described. Experimental subjective testing results are presented that demonstrate the dynamic nature of user perception with adapting multimedia. The results demonstrate that adaptation using the OAT out-performs conventional adaptation strategies in which only a single aspect of the video quality is adapted. In contrast, the OAT provides a mechanism to adapt multiple aspects of the video quality thereby giving better user-perceived quality in both the short and long term. r

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.

Signal Processing: Image Communication, 2015

The usage of HTTP adaptive streaming (HAS) has become widely spread in multimedia services. Because it allows the service providers to improve the network resource utilization and user's Quality of Experience (QoE). Using this technology, the video playback interruption is reduced since the network and server status in addition to capability of user device, all are taken into account by HAS client to adapt the quality to the current condition. Adaptation can be done using different strategies. In order to provide optimal QoE, the perceptual impact of adaptation strategies from point of view of the user should be studied. However, the time-varying video quality due to the adaptation which usually takes place in a long interval introduces a new type of impairment making the subjective evaluation of adaptive streaming system challenging. The contribution of this paper is twofold: first, it investigates the testing methodology to evaluate HAS QoE by comparing the subjective experimental outcomes obtained from ACR standardized method and a semi-continuous method developed to evaluate the long sequences. In addition, influence of using audiovisual stimuli to evaluate the video-related impairment is inquired. Second, impact of some of the adaptation technical factors including the quality switching amplitude and chunk size in combination with high range of commercial content type is investigated. The results of this study provide a good insight toward achieving appropriate testing method to evaluate HAS QoE, in addition to designing switching strategies with optimal visual quality.

IEEE Transactions on Circuits and Systems for Video Technology, 2003

In this paper, we study some of the design tradeoffs of video streaming systems in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the quality of the received video sequence. We also consider the cost to the network user for streaming the video sequence. We have formulated this utility maximization problem as a joint constrained optimization problem where we maximize the difference between the utility and the network cost, subject to the constraint that the decoder buffer does not underflow. In this manner, we can find the optimal tradeoff between video quality and network cost. We present a deterministic dynamic programming approach for both the constant bit rate and renegotiated constant bit rate service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.

2010 Fifth International Conference on Systems and Networks Communications, 2010

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 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 help swiftly localise the causes of a possibly poor quality of experience (QoE). It also can help content providers make decisions regarding the adjustment of vital parameters, such as bit rate and other error correction mechanisms.