scholarly journals User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1387
Author(s):  
Muhamad Hanif Jofri ◽  
Ida Aryanie Bahrudin ◽  
Noor Zuraidin Mohd Safar ◽  
Juliana Mohamed ◽  
Abdul Halim Omar
Keyword(s):  
Video Streaming ◽  
User Satisfaction ◽  
Quality Of Experience ◽  
Video Quality ◽  
Video Content ◽  
Selection Scheme ◽  
Content Selection ◽  
Opinion Score ◽  
Quality Video

Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education,  and entertainment. However,   when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

Quality of Experience vs. QoS in Video Transmission

2010 ◽  
pp. 352-376 ◽  
Author(s):  
André F. Marquet ◽  
Jânio M. Monteiro ◽  
Nuno J. Martins ◽  
Mario S. Nunes
Keyword(s):  
Communication Networks ◽  
Video Transmission ◽  
Quality Of Experience ◽  
Video Quality ◽  
Subjective Assessment ◽  
Ip Networks ◽  
Point Of View ◽  
Qos Parameters ◽  
Quality Video

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

scholarly journals Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Diego José Luis Botia Valderrama ◽  
Natalia Gaviria Gómez
Keyword(s):  
Neural Networks ◽  
Quality Of Experience ◽  
Video Quality ◽  
Similarity Index ◽  
Correlation Coefficients ◽  
Structural Similarity ◽  
Opinion Score ◽  
Measurement And Evaluation ◽  
Rate Correlation

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

scholarly journals Quality of Experience Measurement for Video Streaming Based On Adaptive Neural Fuzzy Inference System

2019 ◽  
pp. 1609-1617
Author(s):  
Rana Fareed Ghani ◽  
Amal Sufiuh Ajrash
Keyword(s):  
Video Streaming ◽  
Fuzzy Inference System ◽  
Quality Of Experience ◽  
Fuzzy Inference ◽  
Technological Development ◽  
Video Quality ◽  
Quality Measurement ◽  
Inference System ◽  
Neural Fuzzy

Technological development in recent years leads to increase the access speed in the networks that allow a huge number of users watching videos online. Video streaming is one of the most popular applications in networking systems. Quality of Experience (QoE) measurement for transmitted video streaming may deal with data transmission problems such as packet loss and delay. This may affect video quality and leads to time consuming. We have developed an objective video quality measurement algorithm that uses different features, which affect video quality. The proposed algorithm has been estimated the subjective video quality with suitable accuracy. In this work, a video QoE estimation metric for video streaming services is presented where the proposed metric does not require information on the original video. This work predicts QoE of videos by extracting features. Two types of features have been used, pixel-based features and network-based features. These features have been used to train an Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the video QoE. 

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

scholarly journals Influence of Resolution and Frame Rate on the Linear In-Stream Video Ad QoE

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Miloš Ljubojević ◽  
Vojkan Vasković ◽  
Zdenka Babić ◽  
Dušan Starčević
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Objective Assessment ◽  
Video Quality ◽  
Rate Change ◽  
Frame Rate ◽  
Strong Impact ◽  
Video Advertising ◽  
The Impact

Abstract: An increasing number of services and facilities that are of interest to users is based on video streaming. Technical characteristics of video have a strong impact on the quality of a video streaming service and its perception by users. The most important measure of quality, which focuses on the user, is the Quality of Experience (QoE). Given that video advertising is a typical video streaming application, it is necessary to analyze the effect of the change of video characteristics on the QoE. This paper examines the impact of resolution and frame rate change on the QoE level by using objective and subjective QoE metrics. It also looks at the possibility of mapping the objective QoE metrics into subjective ones, if the QoE in Internet video advertising is analyzed. It was demonstrated that the values obtained by the objective assessment of quality can be mapped to the results obtained by subjective assessment of quality when the quality of experience of linear in- stream video ads is analyzed. The results indicate that temporal aspects of video quality assessment, e.g. influence of resolution and frame rate change to the level of the QoE, can be achieved by implementation of objective methods. Therefore, quality of experience can be improved by the proper selection of video characteristics values.

scholarly journals VCC-DASH: A Video Content Complexity-Aware DASH Bitrate Adaptation Strategy

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 230
Author(s):  
Juzheng Duan ◽  
Min Zhang ◽  
Jing Wang ◽  
Shuai Han ◽  
Xun Chen ◽  
...  
Keyword(s):  
Quality Of Experience ◽  
Adaptation Strategy ◽  
Video Content ◽  
Buffer Occupancy ◽  
Network Bandwidth ◽  
Opinion Score ◽  
Hypertext Transfer Protocol ◽  
Simulation Results ◽  
Definition Of

Traditional DASH (dynamic adaptation streaming over HTTP(i.e., HyperText Transfer Protocol)) bitrate strategy cannot differentiate segments with different complexities of video content, resulting in the user’s QoE (quality of experience) of segments with high content complexity as worse than that with low content complexity. In case of this, this paper firstly studies video coding and puts forward the definition of video content complexity. Then the effects of content complexity on user’s QoE is analyzed and the QoE utility function of the segment is formulated based on its MOS (mean opinion score, related to the content complexity and bitrate) and bitrate switching between consecutive segments. Last, in order to maximize user’s QoE, this paper proposes VCC-DASH (video content complexity-aware DASH bitrate adaptation strategy) under the constraints of the network bandwidth and the buffer occupancy. In simulations, we compare VCC-DASH with the classical bitrate adaptation strategy proposed by Liu et al. (LIU’s strategy, for short). The simulation results show that the two strategies have similar performances in bitrate switching numbers, playback interruption times, and buffer lengths. In addition, it is more important for simulation results to reveal that VCC-DASH’s average bitrate is much higher than that of LIU’s strategy, which means that VCC-DASH can make fuller use of the network bandwidth than LIU’s strategy does. Moreover, the MOS distribution of the VCC-DASH is more concentrated on the better scores “4~5”, which profit from its content complexity-aware adaptation to allocate more bandwidth resources to high-complexity segments.

Optimizing Quality-of-Experience for HTTP-based Adaptive Video Streaming

2016 ◽  
Vol 7 (4) ◽  
pp. 22-44 ◽  
Author(s):  
Sangeeta Ramakrishnan ◽  
Xiaoqing Zhu ◽  
Frank Chan ◽  
Kashyap Kodanda Ram Kambhatla ◽  
Zheng Lu ◽  
...  
Keyword(s):  
Video Streaming ◽  
Bandwidth Allocation ◽  
Quality Of Experience ◽  
Video Quality ◽  
Bandwidth Management ◽  
Adaptive Video Streaming ◽  
Equal Rate ◽  
Optimized Allocation ◽  
Video Quality Monitoring

In this work, the authors present a novel bandwidth management solution for optimizing overall quality of experience (QoE) of multiple video streaming sessions. Instead of allocating bandwidth equally among competing flows, they propose to tailor the bandwidth allocation to both content complexity of requested video and playout buffer status of individual clients. The authors formulate the multi-client bandwidth allocation problem within the convex optimization framework, which is flexible enough to accommodate a wide variety of video quality metrics. Further, the authors present a practical architecture based on software defined networking (SDN) with two components: video quality monitoring and video quality optimization. Testbed-based experiments confirm that with quality-optimized allocation the network can support up to 75% more users at the same level of quality-of-experience (QoE) than conventional equal-rate allocations.

scholarly journals Real-Time QoE Monitoring System for Video Streaming Services with Adaptive Media Playout

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Mingfu Li ◽  
Chien-Lin Yeh ◽  
Shao-Yu Lu
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Monitoring System ◽  
Quality Of Experience ◽  
Experimental Results ◽  
Media Player ◽  
Opinion Score ◽  
Streaming Services ◽  
The Mean

Quality of Experience (QoE) of video streaming services has been attracting more and more attention recently. Therefore, in this work we designed and implemented a real-time QoE monitoring system for streaming services with Adaptive Media Playout (AMP), which was implemented into the VideoLAN Client (VLC) media player to dynamically adjust the playout rate of videos according to the buffer fullness of the client buffer. The QoE monitoring system reports the QoE of streaming services in real time so that network/content providers can monitor the qualities of their services and resolve troubles immediately whenever their subscribers encounter them. Several experiments including wired and wireless streaming were conducted to show the effectiveness of the implemented AMP and QoE monitoring system. Experimental results demonstrate that AMP significantly improves the QoE of streaming services according to the Mean Opinion Score (MOS) estimated by our developed program. Additionally, some challenging issues in wireless streaming have been easily identified using the developed QoE monitoring system.

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