QDASH: A QoE-aware DASH system

IEEE Transactions on Broadcasting, 2016

Currently, dynamic adaptive streaming over HTTP (DASH) standard is used to change the video resolution according to the network capacity of the end user's. However, if the video resolution changes frequently, the attention of the users can be affected decreasing their quality-of-experience (QoE). In this context, this paper proposes a no-reference video quality metric that takes three impairment factors into account, initial buffering delay, temporal interruptions or pauses, and video resolution changes during a video transmission. Also, the temporal locations of pauses and resolution changes are considered. In order to perform this task, extensive subjective tests were conducted. Experimental results show that the evaluators' QoE is highly correlated with the frequency of pauses, video resolution changes and initial delay. Based on these results a video streaming quality metric for DASH services (VsQM DASH) is formulated and implemented on PC and mobile hand-held devices. Finally, the performance of VsQM DASH is validated with additional subjective tests reaching a person correlation coefficient of 0.99. Furthermore, experimental results demonstrated that the proposed metric has low complexity because it is based on application level parameters and its energy consumption is negligible in relation to the energy consumed by the video player; therefore, can be easily implemented in consumer electronic devices.

Proceedings of the 29th ACM International Conference on Multimedia

Exponential growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the user's perceived quality of such services. Enhancement of experienced quality by the users becomes more substantial when service providers compete on establishing superiority by gaining more subscribers or customers. Quality of Experience (QoE) enhancement would not be possible without an authentic and accurate assessment of the streaming sessions. HTTP Adaptive Streaming (HAS) is today's prevailing technique to deliver the highest possible audio and video content quality to the users. An end-to-end evaluation of QoE in HAS covers the precise measurement of the metrics that affect the perceived quality, e.g., startup delay, stall events, and delivered media quality. Mentioned metrics improvements could limit the service's scalability, which is an important factor in real-world scenarios. In this study, we will investigate the stated metrics, best practices and evaluations methods, and available techniques with an aim to (i) design and develop practical and scalable measurement tools and prototypes, (ii) provide a better understanding of current technologies and techniques (e.g., Adaptive Bitrate algorithms), (iii) conduct in-depth research on the significant metrics in a way that improvements of QoE with scalability in mind would be feasible, and finally (iv) provide a comprehensive QoE model which outperforms state-of-the-art models.

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.

Multimedia Systems

publication browsers, TVs. Each of these platforms has specific requirements with respect to transmission and video quality. Moreover, the environment within which most of the video streaming clients operate is both unreliable and varies over time. However, regardless of the access device, users want the best viewing experience possible. HTTP adaptive streaming (HAS) is the most successful technology so far that allows content providers to cater for the requirements of the multitude of devices and contexts. The process through which a HAS client chooses a video rate is called adaptive bitrate selection (ABR). The first generation of ABRs relied on throughput estimation and selected the highest video rate lower than the measured throughput [14]. This is based on the work of Wang et al. [22] that showed if the available TCP throughput is twice the bitrate of the video plus a few seconds of start-up delay TCP can ensure an acceptable video streaming experience. It later became clear that throughput estimation alone is not a sufficient parameter for designing efficient ABR since an accurate bandwidth estimation above the HTTP layer is difficult to achieve [6]. Consequently, any video rate selection algorithm that solely depends on such a relatively inaccurate estimate results in unnecessary rebuffering events [7], an undesirable variability of video rate [6] and sub-optimal video quality [6]. Various attempts have been made to improve some of the identified issues of throughput-based ABRs by supplementing throughput measurements with information about the playback buffer [1, 10, 21]. Using buffer occupancy as a factor in video rate selection has developed from regarding buffer state changes as a complementary factor in making a rate selection decision [10, 21] to employing it as the sole metric [7, 8]. Though, whatever factor an ABR primarily relies on, it is difficult to build an ABR that maximises Quality of Experience (QoE) without taking buffer state changes into consideration.

IEEE Access

With the recent increased usage of video services, the focus has recently shifted from traditional Quality of Service (QoS) 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 artefacts 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 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. HAS QoE models, influence factors (IF), 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.

Entropy, 2021

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH ...

Image Quality and System Performance XI, 2014

With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients' behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.

Multimedia streaming has major commercial potential as the global community of online video viewers is expanding rapidly following the proliferation of low-cost multimedia-enabled mobile devices. These devices enable increasing amounts of video-based content to be acquired, stored, and distributed across existing best effort networks that also carry other traffic types. Although a number of protocols are used for video transfer, a significant portion of the Internet streaming media is currently delivered over Hypertext Transfer Protocol (HTTP). Network congestion is one of the most important issues that affects networking traffic in general and video content delivery. Among the various solutions proposed, adaptive delivery of content according to available network bandwidth was very successful. In this context, the most recent standardisation efforts have focused on the introduction of the Dynamic Adaptive Streaming over HTTP (DASH) (ISO, 2012) standard. DASH offers support for client-based bitrate video streaming adaptation, but as it does not introduce any particular adaptation mechanism, it relies on third party solutions to complement it. This chapter provides an overview of the DASH standard and presents a short survey of currently proposed mechanisms for video adaptation related to DASH. It also introduces the DASH-aware Performance-Oriented Adaptation Agent (dPOAA), which improves user Quality of Experience (QoE) levels by dynamically selecting best performing sources for the delivery of video content. dPOAA, in its functionality, considers the characteristics of the network links connecting clients with video providers. dPOAA can be utilised as a DASH player plugin or in conjunction with the DASH-based performance-oriented Adaptive Video Distribution solution (DAV) (Rovcanin & Muntean, 2013), which considers the local network characteristics, quantity of requested content available locally, and device and user profiles.

Computer Networks, 2015

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strive to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.

2012 IEEE Consumer Communications and Networking Conference (CCNC), 2012

Video services are being adopted widely in both mobile and fixed networks. For their successful deployment, the content providers are increasingly becoming interested in evaluating the performance of such traffic from the final users' perspective, that is, their Quality of Experience (QoE). For this purpose, subjective quality assessment methods are costly and can not be used in real time. Therefore, automatic estimation of QoE is highly desired. In this paper, we propose a noreference QoE monitoring module for adaptive HTTP streaming using TCP and the H.264 video codec. HTTP streaming using TCP is the popular choice of many web based and IPTV applications due to the intrinsic advantages of the protocol. Moreover, these applications do not suffer from video data loss due to the reliable nature of the transport layer. However, there can be playout interruptions and if adaptive bitrate video streaming is used then the quality of video can vary due to lossy compression. Our QoE estimation module, based on Random Neural Networks, models the impact of both factors. The results presented in this paper show that our model accurately captures the relation between them and QoE.