Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom and has wide applications in many fields such as online education, entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding. To this end, this paper proposes a communication and computation resource allocation scheme for QoE-driven point cloud video streaming. In particular, we maximize system resource utilization by selecting different quantities, transmission forms and quality level tiles to maximize the quality of experience. Extensive simulations are conducted and the simulation results show the superior performance over the existing schemes
HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to …
Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic. The available bandwidths of modern wireless networks are often unstable, leading to difficulties in delivering smooth, high-quality video. Streaming service providers such as Netflix and YouTube attempt to adapt their systems to adjust in response to these bandwidth limitations by changing the video bitrate or, failing that, allowing playback interruptions (rebuffering). Being able to predict end user' quality of experience (QoE) resulting from these adjustments could lead to perceptually-dr…
360-degree video provides an immersive 360-degree viewing experience and has been widely used in many areas. The 360-degree video live streaming systems involve capturing, compression, uplink (camera to video server) and downlink (video server to user) transmissions. However, few studies have jointly investigated such complex systems, especially the rate adaptation for the coupled uplink and downlink in the 360-degree video streaming under limited bandwidth constraints. In this letter, we propose a quality of experience (QoE)-driven 360-degree video live streaming system, in which a video serv…
HTTP Adaptive Streaming (HAS) is nowadays a popular solution for multimedia delivery. The novelty of HAS lies in the possibility of continuously adapting the streaming session to current network conditions, facilitated by Adaptive Bitrate (ABR) algorithms. Various popular streaming and Video on Demand services such as Netflix, Amazon Prime Video, and Twitch use this method. Given this broad consumer base, ABR algorithms continuously improve to increase user satisfaction. The insights for these improvements are, among others, gathered within the research area of Quality of Experience (QoE). Wit…
MPEG has recently proposed Server and Network Assisted Dynamic Adaptive Streaming over HTTP (SAND-DASH) for video streaming over the Internet. In contrast to the purely client-based video streaming in which each client makes its own decision to adjust its bitrate, SAND-DASH enables a group of simultaneous clients to select their bitrates in a coordinated fashion in order to improve resource utilization and quality of experience. In this paper, we study the performance of such an adaptation strategy compared to the traditional approach with large number of clients having mobile Internet access.…
Point cloud video has been widely used by augmented reality (AR) and virtual reality (VR) applications as it allows users to have an immersive experience of six degrees of freedom (6DoFs). Yet there is still a lack of research on quality of experience (QoE) model of point cloud video streaming, which cannot provide optimization metric for streaming systems. Besides, position and color information contained in each pixel of point cloud video, and viewport distance effect caused by 6DoFs viewing procedure make the traditional objective quality evaluation metric cannot be directly used in point c…
With the merit of containing full panoramic content in one camera, Virtual Reality (VR) and 360-degree videos have attracted more and more attention in the field of industrial cloud manufacturing and training. Industrial Internet of Things (IoT), where many VR terminals needed to be online at the same time, can hardly guarantee VR's bandwidth requirement. However, by making use of users' quality of experience (QoE) awareness factors, including the relative moving speed and depth difference between the viewpoint and other content, bandwidth consumption can be reduced. In this paper, we propose …
In video streaming services, predicting the continuous user's quality of experience (QoE) plays a crucial role in delivering high quality streaming contents to the user. However, the complexity caused by the temporal dependencies in QoE data and the non-linear relationships among QoE influence factors has introduced challenges to continuous QoE prediction. To deal with that, existing studies have utilized the Long Short-Term Memory model (LSTM) to effectively capture such complex dependencies, resulting in excellent QoE prediction accuracy. However, the high computational complexity of LSTM, c…
Scalable video coding has drawn great interest in content delivery in many multimedia services thanks to its capability to handle terminal heterogeneity and network conditions variation. In our previous work, and under the umbrella of ENVISION, we have proposed a playout smoothing mechanism to ensure the uniform delivery of the layered stream, by reducing the quality changes that the stream undergoes when adapting to changing network conditions. In this paper we study the resulting video quality, from the final user perception under different network conditions of loss and delays. For that we …
We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging ap…
Proactive tile-based virtual reality (VR) video streaming employs the current tracking data of a user to predict future requested tiles, then renders and delivers the predicted tiles to be requested before playback. The quality of experience (QoE) depends on the overall performance of prediction, computing (i.e., rendering) and communication. All prior works neglect that users may have privacy requirement, i.e., not all the current tracking data are allowed to be uploaded. In this paper, we investigate the privacy-aware VR streaming. We first establish a dataset that collects the privacy requi…
The fundamental conflict between the enormous space of adaptive streaming videos and the limited capacity for subjective experiment casts significant challenges to objective Quality-of-Experience (QoE) prediction. Existing objective QoE models exhibit complex functional form, failing to generalize well in diverse streaming environments. In this study, we propose an objective QoE model namely knowledge-driven streaming quality index (KSQI) to integrate prior knowledge on the human visual system and human annotated data in a principled way. By analyzing the subjective characteristics towards str…
The live streaming is more challenging than on-demand streaming, because the low latency is also a strong requirement in addition to the trade-off between video quality and jitters in playback. To balance several inherently conflicting performance metrics and improve the overall quality of experience (QoE), many adaptation schemes have been proposed. Bitrate adaptation is one of the major solutions for video streaming under time-varying network conditions, which works even better combining with some latency control methods, such as adaptive playback rate control and frame dropping. However, it…
We take an analytical approach to study Quality of user Experience (QoE) for video streaming applications. First, we show that random linear network coding applied to blocks of video frames can significantly simplify the packet requests at the network layer and save resources by avoiding duplicate packet reception. Network coding allows us to model the receiver's buffer as a queue with Poisson arrivals and deterministic departures. We consider the probability of interruption in video playback as well as the number of initially buffered packets (initial waiting time) as the QoE metrics. We char…
Proactive tile-based virtual reality (VR) video streaming can use the trace of FoV and eye movement to predict future requested tiles, then renders and delivers the predicted tiles before playback. The quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources. Recently, it has been found that with the FoV and eye movement data collected for a user, one can infer the identity and preference of the user. Existing works investigate the privacy protection for eye movement, but never address how to protect the privacy in terms of FoV and how the privacy pr…
HTTP Adaptive Streaming (HAS) has become a cost-effective means for multimedia delivery nowadays. However, how the quality of experience (QoE) is jointly affected by 1) varying perceptual quality and 2) interruptions is not well-understood. In this paper, we present the first attempt to quantitatively quantify the relative impacts of these factors on the QoE of streaming sessions. To achieve this purpose, we first model the impacts of the factors using histograms, which represent the frequency distributions of the individual factors in a session. By using a large dataset, various insights into…
Cardiff Metropolitan University（卡迪夫城市大学）；University of Western Australia（西澳大利亚大学）；Dublin City University（都柏林城市大学）；University College Dublin（都柏林大学学院）；University of Cagliari（卡利亚里大学）
The Quality of Experience (QoE) based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction and resource management where machine learning (ML) can play a crucial role. In this paper, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. Firstly, we provide a detailed pipeline for developing and deploying supervised learning-based video streamin…
Although HTTP-based video streaming can easily penetrate firewalls and profit from Web caches, the underlying TCP may introduce large delays in case of a sudden capacity loss. To avoid an interruption of the video stream in such cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream such that future frames are downloaded at any instant. This pre-fetched content maintains a fluent video stream even under highly variant network conditions and leads to high Quality of Experience (QoE). We characteri…
Existing reinforcement learning~(RL)-based adaptive bitrate~(ABR) approaches outperform the previous fixed control rules based methods by improving the Quality of Experience~(QoE) score, as the QoE metric can hardly provide clear guidance for optimization, finally resulting in the unexpected strategies. In this paper, we propose \emph{Tiyuntsong}, a self-play reinforcement learning approach with generative adversarial network~(GAN)-based method for ABR video streaming. Tiyuntsong learns strategies automatically by training two agents who are competing against each other. Note that the competit…
University of Waterloo（滑铁卢大学）；Shanghai Jiao Tong University（上海交通大学）；Ningbo University（宁波大学）
Quality of experience (QoE) assessment for adaptive video streaming plays a significant role in advanced network management systems. It is especially challenging in case of dynamic adaptive streaming schemes over HTTP (DASH) which has increasingly complex characteristics including additional playback issues. In this paper, we provide a brief overview of adaptive video streaming quality assessment. Upon our review of related works, we analyze and compare different variations of objective QoE assessment models with or without using machine learning techniques for adaptive video streaming. Throug…
HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide su…
The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models. Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices. We collect human opinions for each video with a series of carefully designed subjective experiments. Subsequent data analysis and testing/comparison of ABR algorithms and …
The knowledge of future throughput variations in mobile networks becomes more and more possible today thanks to the rich contextual information provided by mobile applications and services and smartphone sensors. It is even likely that such contextual information, which may include traffic, mobility and radio conditions will lead to a novel agile resource management not yet thought of. In this paper, we propose an framework (called NEWCAST) that anticipates the throughput variations to deliver video streaming content. We develop an optimization problem that realizes a fundamental trade-off amo…
In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of H…
Point cloud video transmission is challenging due to high encoding/decoding complexity, high video bitrate, and low latency requirement. Consequently, conventional adaptive streaming methodologies often find themselves unsatisfactory to meet the requirements in threefold: 1) current algorithms reuse existing quality of experience (QoE) definitions while overlooking the unique features of point cloud video thus failing to provide optimal user experience, 2) most deep learning approaches require long-span data collections to learn sufficiently varied network conditions and result in long trainin…
The increasing demand for video streaming services with high Quality of Experience (QoE) has prompted a lot of research on client-side adaptation logic approaches. However, most algorithms use the client's previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. Additionally, we show how crowd information can be integrated into existing algorithms and illustrate this with two state-of-the-art algorithms. We evaluate our algorithm and state-of-the-art algorithms (including our mo…
The cache and transcoding of the multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact and determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients in MEC networks. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients' QoE. Therefore, we propose a joint optimization framework of video segment cache and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop a MEC cache man…
Proactive tile-based virtual reality (VR) video streaming employs the current tracking data of a user to predict future requested tiles, then renders and delivers the predicted tiles before playback. Very recently, privacy protection in proactive VR video streaming starts to raise concerns. However, existing privacy protection may fail even with privacy-preserve federated learning. This is because when the future requested tiles can be predicted accurately, the user-behavior-related data can still be recovered from the predicted tiles. In this paper, we consider how to protect privacy even wit…
Multimedia streaming to mobile devices is challenging for two reasons. First, the way content is delivered to a client must ensure that the user does not experience a long initial playback delay or a distorted playback in the middle of a streaming session. Second, multimedia streaming applications are among the most energy hungry applications in smartphones. The energy consumption mostly depends on the delivery techniques and on the power management techniques of wireless access technologies (Wi-Fi, 3G, and 4G). In order to provide insights on what kind of streaming techniques exist, how they …
Recently, multi-screen cloud social TV is invented to transform TV into social experience. People watching the same content on social TV may come from different locations, while freely interact with each other through text, image, audio and video. This crucial virtual living-room experience adds social aspects into existing performance metrics. In this paper, we parse social TV user experience into three elements (i.e., inter-user delay, video quality of experience (QoE), and resource efficiency), and provide a joint analytical framework to enhance user experience. Specifically, we propose a c…
Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as an M/M/1 queue, plus the consideration of bursty arrivals. When the buffer is empty, the service restarts after a certain amount of packets are \emph{prefetched}. With this goal, we propose two approaches to obtain the \emph{exact distribution} of the number of buffer starvations, one of which is based on \emph{Ballot theorem}, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playbac…
We propose a robust scheme for streaming 360-degree immersive videos to maximize the quality of experience (QoE). Our streaming approach introduces a holistic analytical framework built upon the formal method of stochastic optimization. We propose a robust algorithm which provides a streaming rate such that the video quality degrades below that rate with very low probability even in presence of uncertain head movement, and bandwidth. It assumes the knowledge of the viewing probability of different portions (tiles) of a panoramic scene. Such probabilities can be easily derived from crowdsourced…
Immersive media streaming, especially virtual reality (VR)/360-degree video streaming which is very bandwidth demanding, has become more and more popular due to the rapid growth of the multimedia and networking deployments. To better explore the usage of resource and achieve better quality of experience (QoE) perceived by users, this paper develops an application-layer scheme to jointly exploit the available bandwidth from the LTE and Wi-Fi networks in 360-degree video streaming. This newly proposed scheme and the corresponding solution algorithms utilize the saliency of video, prediction of u…
Inferring the quality of network services is the vital basis of optimization for network operators. However, prevailing real-time video streaming applications adopt encryption for security, leaving it a problem to extract Quality of Service (QoS) indicators of real-time video. In this paper, we propose DaI, a traffic-based real-time video quality estimator. DaI can partially decrypt the encrypted real-time video data and applies machine learning methods to estimate key objective Quality of Experience (QoE) metrics of real-time video. According to the experimental results, DaI can estimate obje…
Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming providers' profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Moreover, allocating the exact needed resources beforehand avoi…
A rapid increase in the video traffic together with an increasing demand for higher quality videos has put a significant load on content delivery networks in the recent years. Due to the relatively limited delivery infrastructure, the video users in HTTP streaming often encounter dynamically varying quality over time due to rate adaptation, while the delays in video packet arrivals result in rebuffering events. The user quality-of-experience (QoE) degrades and varies with time because of these factors. Thus, it is imperative to monitor the QoE continuously in order to minimize these degradatio…
Dynamic Adaptive Streaming over HTTP (DASH) is a video streaming technique largely used. One key point is the adaptation mechanism which resides at the client's side. This mechanism impacts greatly on the overall Quality of Experience (QoE) of the video streaming. In this paper, we propose a new adaptation algorithm for DASH, namely SSIM Based Adaptation (SBA). This mechanism is user-experience driven: it uses the Structural Similarity Index Measurement (SSIM) as main video perceptual quality indicator; moreover, the adaptation is based on a joint consideration of SSIM indicator and the physic…
Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Dynamic adaptive streaming over HTTP provides the work of most multimedia services, however, the nature of this technology further complicates the assessment of the QoE (Quality of Experience). In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studied. The paper presents standard and handcrafted features, shows their correlation and p-Value of significance. VQA (Video Quality Assessment) models based on regression and gradient boosting with SRCC reaching up to 0.9647 on the validation subsample are proposed. The proposed reg…
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 un…
We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the problem of jointly optimizing network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using m…
In mobile networks, users may lose coverage when entering a building due to the high signal attenuation at windows and walls. Under such conditions, services with minimum bit-rate requirements, such as video streaming, often show poor Quality-of-Experience (QoE). We will present a Bayesian detector that combines measurements from two Smartphone sensors to decide if a user is inside a building or not. Based on this coverage classification, we will propose an HTTP adaptive streaming (HAS) algorithm to increase playback stability at a high average bitrate. Measurements in a typical office buildin…
Multimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user QoE many researchers deploy subjective quality assessments where participants watch and rate videos artificially infused with various temporal and spatial impairments. To aid current efforts in bridging the gap between the mapping of objective video QoE metrics to user experience, we developed DashReStreamer, an open-source framework for re-creati…
In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary t…
Adaptive bitrate streaming (ABR) has become thede factotechnique for videostreaming over the Internet. Despite a flurry of techniques, achieving high quality ABRstreaming over cellular networks remains a tremendous challenge. First, the design ofan ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics suchas video quality, quality changes, stalls and startup performance, which is even harderunder highly dynamic bandwidth in cellular network. Second, streaming providers havebeen moving towards using Variable Bitrate (VBR) encodings for the video content,which introduces ne…
