With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.

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QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges

Over the last few years, the evolution of network and user handsets’ technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and added-value services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Network coding is an elegant and novel technique to improve network throughput and performance It is considered as a critical technology to facilitate ever-increasing demands of future wireless networks It exploits the broadcast nature of wireless media and cooperatively codes packets from different senders to provide reliable, secure, and efficient transmissions Current research focuses on either transmission delay, coding complexity, forwarding security, or end-to-end throughput Network coding-aided solutions can recover lost packets without feedback, eliminate latency, reduce the routing cost on diverse paths, or optimize the capacity of unstable wireless networks However, devices or smart sensors usually have limited computational capacity and some applications could not tolerate high decoding delay, which prevents network coding from being widely deployed in the real world In recent years, many research methods consider simplifying decoding matrix or coding algorithm to alleviate the shortcoming of network coding and further satisfy the extreme demands of the future wireless network This article summarizes complexity-optimized methods and explains the interaction effect of coding opportunities and decoding overhead We propose a taxonomy of practical network coding methods and illustrate three practical directions on cutting computational complexity and enhancing progressive decoding We also conclude the benefit and cost of current network coding algorithms along with the outline of future research

Practical Network Coding Technologies and Softwarization in Wireless Networks

The use of adaptive-bitrate-streaming services over networks has been increasing in recent years. The quality of adaptive-bitrate-streaming services is primarily affected by the video resolution, the audio and video bitrate, bitrate adaptation, stalling due to a lack of playout buffer, and the content length. Therefore, service providers should monitor quality in real time to confirm the normality of their services. To accurately monitor quality, a model that can be used for quality estimation should be developed. To develop such a model, we first conducted extensive subjective quality assessment tests. We then developed a model using the subjective data obtained in the tests. Finally, we verified the performance of the proposed model by applying it to unknown datasets (different from the training datasets used to develop the model) and confirmed its high quality-estimation accuracy.

Parametric Quality-Estimation Model for Adaptive-Bitrate-Streaming Services

In the context of HTTP Adaptive Streaming (HAS), a main challenge is how to evaluate the quality of experience (QoE) of a session. In general, the QoE of a streaming session is affected by three key factors, namely initial delay, varying perceptual quality, and interruptions. In this paper, in order to investigate the impacts of quality variations and interruptions on the QoE of a session, we propose to use histograms, which represent the frequency distributions of these recurrent factors in a session. The QoE of a session is modeled by pooling the statistics of the recurrent factors and the initial delay. Evaluation results show that the proposed model has a high prediction performance. Especially, the proposed model provides insights into the impacts of different influence factors, thus leading to suggestions to improve the quality of streaming video.

A Multi-Factor QoE Model for Adaptive Streaming over Mobile Networks

In this paper, we propose a Quality of Experience (QoE) model for adaptive streaming services based on bitrate distribution to evaluate customers' subjective perception accurately Unlike the QoE assessment for constant bitrate (CBR) video services, different bitrate distribution causes significant difference for the QoE of adaptive streaming Therefore, we design a method based on the Primacy Effect and Recency Effect, a psychological phenomenon that the initial and recent information are more prominent in short-term memory for people, to analyze and quantify the QoE influence caused by bitrate distribution Besides the bitrate distribution, real-time bitrate and video content types are also mapped into our QoE model to provide great QoE evaluation for adaptive streaming We evaluate our QoE model via plenty of subjective Mean Opinion Score (MOS) tests, which include 16 test samples with 659 votes The Pearson Correlation Coefficient between our QoE score and MOS achieves to 097, which indicates that our model can evaluate customers' perception on adaptive streaming quality accurately Besides, we compare our QoE model to the average QoE evaluation method Simulation results show that our QoE model is more flexible and powerful to reflect customers' feeling on adaptive streaming services with various kinds of bitrate distributions

A method of QoE evaluation for adaptive streaming based on bitrate distribution

The growing demand on video streaming services is the main motive power for video quality assessment. Due to the rapid development of video streaming services, customers are more enjoying higher quality videos, and their perceptions on video service will directly influence the service provider's performance, which makes it significant to study end-users' subjective perception, named as Quality of Experience (QoE), on video streaming for both service providers and end-users. In this paper, a QoE evaluation model is proposed to predict the end-users' perception on video streaming service considering different video content types. This QoE model, named as Video-Mean Opinion Score (VMOS) model, directly focuses on end-users' feeling. Thus, the Key Performance Indicators (KPIs), which can be straightly felt by end-users, are mapped to QoE score without considering the network parameters. The excellent performance of VMOS model for QoE evaluation on video quality has been verified via plenty subjective Mean Opinion Score (MOS) test, which includes 180 video samples with 1280 valid votes. The Pearson Correlation Coefficient between VMOS score and MOS is as high as 0.925, which indicates that this model can evaluate users' perception on video quality with almost the same accuracy as subjective test.

QoE-based evaluation model on video streaming service quality

Quality of Experience Study on Dynamic Adaptive Streaming Based on HTTP

Nowadays, adaptive streaming is quite popular and is expected to develop at a high rate of speed Dynamic Adaptive Streaming over HTTP (DASH) is a new 3GPP and MPEG standard for adaptive steaming over HTTP which is currently being extensively accepted in the industry DASH requests best quality of media under available bandwidth with adaptive algorithm which is implemented in clients To obtain the appropriate quality of media, an accurate estimate of the bandwidth is needed In this paper, we present a novel algorithm to estimate the bandwidth even more accurately This bandwidth estimation algorithm involves bandwidth detective method and bandwidth computing method Outliers within a short term, slight fluctuations and persistent variations can be distinguished through our proposed bandwidth detective method Hereafter, we compute the estimated bandwidth in different methods based on the result of bandwidth detection We build a DASH platform and compare our proposed algorithm to the VLC adaptive algorithm According to the results, it is demonstrated that our algorithm possesses more accurate bandwidth estimation and higher buffer level Our proposed algorithm is sensitive to persistent variations and robust to slight fluctuations

Bandwidth estimation of rate adaption algorithm in DASH

The performance of Perceptual Evaluation of Speech Quality (PESQ), a most prevailing voice service Quality of Experience (QoE) assessment method, is evaluated in Chinese environment. A normative Chinese speech database has been developed, especially considered the distortions from the real transmission situation. Through the huge subjective result, a distinct conclusion can be drawn that it is not convinced to use PESQ assess Chinese voice service directly. A non-linear compensation factor is proposed in this paper to make the universal PESQ be suitable for the Chinese voice service accurately. After compensation, the score of PESQ matches the Mean Opinion Score (MOS) much more, and the residual error distribution has also been improved greatly compared with the result given in ITU P.862.1.

Yun Shen

Papers

A method of QoE evaluation for adaptive streaming based on bitrate distribution

QoE-based evaluation model on video streaming service quality

Quality of Experience Study on Dynamic Adaptive Streaming Based on HTTP

Bandwidth estimation of rate adaption algorithm in DASH

Performance Evaluation and Accuracy Upgrading of PESQ in Chinese Environment