• Journal of KIISE
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• v.44 no.1
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• pp.95-106
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• 2017

HTTP adaptive streaming has recently emerged to handle the rapidly growing traffic and to provide high quality multimedia contents. To improve the QoE (Quality of Experience) for HTTP adaptive streaming service, the average video bitrate should be maximized, and the video switching frequency (difference of bitrate between adjacent segments) and video stalling events need to be minimized. The recently proposed quality adaptation algorithms for HTTP adaptive streaming do not provide high QoE, since detailed QoE factors such as video switching frequency and bitrate difference of adjacent segments, are not considered. In this paper, we propose a SQA (Smooth Quality Adaptation) algorithm to improve the user QoE. The proposed algorithm provides the smoothed QoE, such that it minimizes the unnecessary video switching events by maintaining the quality in a certain period, thus minimizing the bitrate difference of adjacent segments. Through simulation, we confirm that the proposed algorithm reduces the unnecessary switching events, and prevents the sudden decrease in video quality.

• 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.

• 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.

• 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.

• ETRI Journal
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• v.43 no.3
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• pp.447-458
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• 2021

The quality of experience (QoE) of video streaming is degraded by playback interruptions, which can be mitigated by the playout buffers of end users. To analyze the impact of playout buffer dynamics on the QoE of wireless adaptive hypertext transfer protocol (HTTP) progressive video, we model the playout buffer as a G/D/1 queue with an arbitrary packet arrival rate and deterministic service time. Because all video packets within a block must be available in the playout buffer before that block is decoded, playback interruption can occur even when the playout buffer is non-empty. We analyze the queue length evolution of the playout buffer using diffusion approximation. Closed-form expressions for user-perceived video quality are derived in terms of the buffering delay, playback duration, and interruption probability for an infinite buffer size, the packet loss probability and re-buffering probability for a finite buffer size. Simulation results verify our theoretical analysis and reveal that the impact of playout buffer dynamics on QoE is content dependent, which can contribute to the design of QoE-driven wireless adaptive HTTP progressive video management.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• Journal of KIISE
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• v.45 no.2
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• pp.187-194
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• 2018

Recently, Hypertext Transfer Protocol (HTTP) adaptive streaming service is attracting attention. The existing quality adaptive scheme of HTTP adaptive streaming service adjusts the video quality according to the network bandwidth or the client buffer size. However, the problem with the existing quality adaptive scheme is the QoE (Quality of Experience) degradation caused by the unnecessary quality change that occurs due to frequent bandwidth change or fixed buffer threshold. We propose a video quality maintenance scheme that improves average video quality and minimizes unnecessary quality change in order to improve the QoE of HTTP adaptive streaming service in the changing network environment. The proposed scheme maintains high quality for a long time by setting the quality maintenance duration to be long when buffer occupancy and video quality are high. The experimental results show that the proposed scheme improves QoE by improving the average video quality and minimizing the quality change.

• Journal of KIISE
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• v.44 no.4
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• pp.423-432
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• 2017

Recently, the video quality control schemes for the improvement of the QoE (Quality of Experience) of video streaming services that are based on DASH (Dynamic Adaptive Streaming over HTTP), which is a standard of HTTP adaptive streaming (HAS) services, have been studied. However, the problem of the existing schemes is the degradation that is due to unnecessary quality changes because the VBR (Variable Bitrate) characteristics of the video are not considered. In this paper, we propose a SC-DASH (Segment Characteristics-based DASH) which controls the video quality based on the segment characteristics. The SC-DASH can prevent the occurrence of the unnecessary quality changes by controlling the video quality based on the size of the next segment, the segment throughput, and the buffer occupancy. The experiment results showed that the SC-DASH improves the QoE by reducing the unnecessary quality changes compared with the existing quality control schemes.

• 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.1
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• pp.82-87
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• 2015

There are many current researches that are looking to improve the quality of HTTP adaptive streaming services. However, the existing schemes have a serious problem of QoE (Quality of Experience) degradation because few consider VBR video transmission. To cope with this problem, in this paper, we proposed a novel media quality adaptation scheme called CB-DASH (Content and Buffer-aware DASH). The proposed scheme controlled the video quality considering the VBR characteristics of video and the client's buffer state. Through the simulation, we proved that our scheme accomplished a more accurate estimated bandwidth than the conventional DASH and improved the QoE of streaming service.