• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2513-2530
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• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5B
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• pp.451-463
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• 2011

QoE is defined as a quality perceived by users on a certain service. However, standard method to measure voice QoE(MOS) has been developed, but no standard method to measure video QoE has been defined. In this paper, we propose an efficient algorithm to measure video QoE s automatically for IPTV services. The proposed method selects candidate scenarios that affect the users' MOS directly, and derives weight factors for the selected scenarios. With the weight factors for the scenarios, video QoE value is calculated. For the validation of the proposed algorithm, we made degraded videos reflecting the parameters. With the degraded videos, by comparing the user perceived MOSs with the video QoEs derived by the proposed algorithm, we show that the two values are highly correlated each other.

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.18-30
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• 2015

As the speed of networks that subscribers can use has greatly increased, demand for high-quality broadcast content, such as from Internet Protocol Television (IPTV) and Video on Demand (VoD), is likewise increasing. Therefore, while broadcasters are increasing content and channels, they are striving to improve consumer quality of experience (QoE) to differentiate themselves from competitors, including by producing higher physical-quality content. Recently, subjective measurement methods have been internationally standardized as the most reliable approach for measuring and evaluating IPTV QoE. However, a majority of these methods are performed in experimental environments and are based on the extremely brief viewing period of approximately ten seconds using original reference videos. It is actually difficult to apply standard evaluation methods based on a ten-second viewing interval to assess real broadcast watching of IPTV or other services that involve a longer time (i.e., more than thirty minutes). In this paper, we therefore propose a method that accommodates actual viewing environments. Using the mean opinion score, we experimentally analyze the effects of evaluation interval changes under actual conditions in which IPTV service is provided. In addition, we propose improvements by applying the results into actual live broadcast IPTV service and by analyzing consumer service QoE.

• KIISE Transactions on Computing Practices
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• v.22 no.7
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• pp.310-314
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• 2016

Currently, several research studies are looking to improve the quality of DASH (Dynamic Adaptive Streaming over HTTP) based live streaming services. However, conventional DASH based streaming schemes cannot provide seamless playback while maintaining the low buffering and it adversely affects the QoE (Quality of Experience). To address this problem, we propose the QoE driven segment scheduling scheme. The proposed scheme adaptively schedules the segment request message according to the time and variation of segment fetching. Simulation results indicate that the proposed scheme improves the QoE of live streaming service by maintaining the low buffering delay and reducing the buffer underflow events.

• Journal of KIISE
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• v.45 no.2
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• pp.175-186
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• 2018

Recently, HAS (HTTP Adaptive Streaming) has been the subject of much attention to improve the QoE (Quality of Experience). In a high latency network, HAS degrades the QoE due to the lost RTT cycle since it replies with a response of one segment to the request of one segment. The server-push based HAS schemes of downloading multiple segments in one request cause QoE degradation due to the buffer underflow. In this paper, we propose a VSSDS (Video Streaming Scheme based on Dynamic Server-push) scheme to improve the QoE in a high latency network. The proposed scheme adjust video quality by estimating available bandwidth and determine the number of segments to be downloaded for each segment request cycle. Through the simulation, the proposed scheme not only improves the average video bitrate but also alleviates the buffer underflow.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.780-785
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• 2015

Recently HTTP (Hypertext Transfer Protocol) adaptive streaming services have been the subject of much attention. The video quality control scheme of conventional HTTP adaptive streaming services estimates bandwidth using segment throughput and smooths out the sample of segment throughput. However, the conventional scheme has the problem of QoE (Quality of experience) degradation occurring with buffer underflow and frequent quality change due to the fixed number of samples. In order to solve this problem, we propose a video quality control scheme based on segment throughput and buffer occupancy. The proposed scheme determines the number of samples according to the variation of segment throughput. The proposed scheme also controls video quality based on the threshold of bitrate to keep stable buffer occupancy. The simulation results show that proposed scheme improves QoE by preventing buffer underflow and decreasing quality change when compared with the conventional scheme.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.646-656
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• 2008

In this paper, the prototype of haptic-based NVE (networked virtual environment) QoE (quality of experience) is proposed. The proposed framework plays a role of providing users with realistic feeling by managing the system and network resources efficiently under time-varying networks and heterogeneous systems. Therefore, first IoPH (importance of presence for haptic interaction) is defined to quantitate the sense of real that the users feel now. Then we define the haptic-based NVE components required to satisfy the haptic interaction QoE requirements. Finally, QoE adaptation scheme is suggested, which adapts the haptic-based NVE components to current network and system constraints for better haptic interaction quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1351-1372
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• 2022

A self-organizing multiple unmanned aerial vehicles (multi-UAVs) deployment based on virtual forces has a difficulty in ensuring the quality-of-experience (QoE) of users because of the difference between the assumed center for users in a hotspot and an actual center for users in the hotspot. This discrepancy is aggravated in a non-uniform and mobile user distribution. To address this problem, we propose a new density based virtual force (D-VF) multi-UAVs deployment algorithm which employs a mean opinion score (MOS) as a metric of QoE. Because MOS is based on signal-to-noise ratio (SNR), a sum of users' MOS is a good metric not only to secure a wide service area but to enhance the link quality between multi-UAVs and users. The proposed algorithm improves users' QoE by combining virtual forces with a random search force for the exploration of finding multi-UAVs' positions which maximize the sum of users' MOS. In simulation results, the proposed deployment algorithm shows the convergence of the multi-UAVs into the position of maximizing MOS. Therefore, the proposed algorithm outperforms the conventional virtual force-based deployment scheme in terms of QoE for non-uniform user distribution scenarios.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.446-456
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• 2007

Increased access to broadband networks has led to a fast-growing demand for voice and video over IP(VVoIP) applications such as Internet telephony(VoIP), videoconferencing, and IP television(IPTV). For pro-active troubleshooting of VVoIP performance bottlenecks that manifest to end-users as performance impairments such as video frame freezing and voice dropouts, network operators cannot rely on actual end-users to report their subjective quality of experience(QoE). Hence, automated and objective techniques that provide real-time or online VVoIP QoE estimates are vital. Objective techniques developed to-date estimate VVoIP QoE by performing frame-to-frame peak-signal-to-noise ratio(PSNR) comparisons of the original video sequence and the reconstructed video sequence obtained from the sender-side and receiver-side, respectively. Since processing such video sequences is time consuming and computationally intensive, existing objective techniques cannot provide online VVoIP QoE. In this paper, we present a novel framework that can provide online estimates of VVoIP QoE on network paths without end-user involvement and without requiring any video sequences. The framework features the "GAP-model", which is an offline model of QoE expressed as a function of measurable network factors such as bandwidth, delay, jitter, and loss. Using the GAP-model, our online framework can produce VVoIP QoE estimates in terms of "Good", "Acceptable", or "Poor"(GAP) grades of perceptual quality solely from the online measured network conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5392-5418
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• 2017

The primary aim of this paper is to propose a non-linear regression based technique for mapping different network Quality of Service (QoS) factors to an integrated end-user Quality of Experience (QoE) or Mean Opinion Score (MOS) value for an online video streaming service on a mobile phone. We use six network QoS factors for finding out the user QoE. The contribution of this paper is threefold. First, we investigate the impact of the network QoS factors on the perceived video quality. Next, we perform an individual mapping of the significant network QoS parameters obtained in stage 1 to the user QoE based upon a non-linear regression method. The optimal QoS to QoE mapping function is chosen based upon a decision variable. In the final stage, we evaluate the integrated QoE of the system by taking the combined effect of all the QoS factors considered. Extensive subjective tests comprising of over 50 people across a wide variety of video contents encoded with H.265/HEVC and VP9 codec have been conducted in order to gather the actual MOS data for the purpose of QoS to QoE mapping. Our proposed hybrid model has been validated against unseen data and reveals good prediction accuracy.