Layer-Assisted Video Quality Adaptation for Improving QoE in Wireless Networks

The Hypertext Transfer Protocol (HTTP) Adaptive Streaming (HAS) has now become ubiquitous and accounts for a large amount of video delivery over the Internet. But since the Internet is prone to bandwidth variations, HAS's up and down switching between different video bitrates to keep up with bandwidth variations leads to a reduction in Quality of Experience (QoE). In this article, we propose a video bitrate adaptation and prediction mechanism based on Fuzzy logic for HAS players, which takes into consideration the estimate of available network bandwidth as well as the predicted buffer occupancy level in order to proactively and intelligently respond to current conditions. This leads to two contributions: First, it allows HAS players to take appropriate actions, sooner than existing methods, to prevent playback interruptions caused by buffer underrun, reducing the ON-OFF traffic phenomena associated with current approaches and increasing the QoE. Second, it facilitates fair sharing of bandwidth among competing players at the bottleneck link. We present the implementation of our proposed mechanism and provide both empirical/QoE analysis and performance comparison with existing work. Our results show that, compared to existing systems, our system has (1) better fairness among multiple competing players by almost 50% on average and as much as 80% as indicated by Jain's fairness index and (2) better perceived quality of video by almost 8% on average and as much as 17%, according to the estimate the Mean Opinion Score (eMOS) model.

HTTP Adaptive Streaming(HAS) has become widely deployed in multimedia services for its ability for providing the best Quality of Experience(QoE) for viewers with limited resource, especially in fluctuating wireless networkings. With its client-driven rate adaptation mechanism, HAS is able to adapt to fluctuating network condition and various terminals dynamically. The rate adaptation algorithm is crucial in HAS for its direct impact on the QoE. Most of researches focus on optimizing objective indicators, such as elevating mean bit-rate, lessening playout interruption, and avoiding quality switches, etc. However, the promotion in objective indicators doesn't indicate better experience of end users directly. In this paper, a novel QoE-Optimal Rate Adaptation(QORA) algorithm is proposed to achieve the best QoE. Firstly, a QoE model with iterative form is deduced for on-line evaluation. According to this model, the QORA is able to predict the possible QoE of each bit-rate level in candidate, then greedily choose the one with the highest QoE. The perceptual quality of HAS is optimized by the QORA directly and quantitatively. The simulation result shows that the proposed QORA is able to provide the optimal QoE compared with other rate adaptation schemes, although some of them have better performance in a single objective indicator.

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

The growing consumer demand for mobile video services is one of the key drivers of the evolution of new wireless multimedia solutions requiring exploration of new ways to optimize future wireless networks for video services towards delivering enhanced quality of experience (QoE). One of these key video enhancing solutions is HTTP adaptive streaming (HAS), which has recently been spreading as a form of Internet video delivery and is expected to be deployed more broadly over the next few years. As a relatively new technology in comparison with traditional push-based adaptive streaming techniques, deployment of HAS presents new challenges and opportunities for content developers, service providers, network operators and device manufacturers. One of these important challenges is developing evaluation methodologies and performance metrics to accurately assess user QoE for HAS services, and effectively utilizing these metrics for service provisioning and optimizing network adaptation. In that vein, this article provides an overview of HAS concepts, and reviews the recently standardized QoE metrics and reporting framework in 3GPP. Furthermore, we present an end-to-end QoE evaluation study on HAS conducted over 3GPP LTE networks and conclude with a discussion of future challenges and opportunities in QoE optimization for HAS services.

Recently, with the spread of smart devices and the development of networks, the demand for video streaming has increased, and HTTP adaptive streaming has been gaining attention. HTTP adaptive streaming can guarantee QoE (Quality of Experience) because it selects the video quality according to the network state. However, in wireless networks, delay and packet loss rates are high and the available bandwidth fluctuates sharply. Therefore, QoE is degraded when the quality is selected on the basis of the measured bandwidth. In this paper, we propose an adaptive quality control scheme to improve QoE of video streaming in wireless networks. The proposed scheme calculates two factors, the buffer underflow probability and the instability, by considering the buffer state and the changes of quality level. Using these factors, the proposed scheme defines a quality control region that consists of four sub-regions. The video quality is determined by applying different control strategy to each sub-region. The results of experiments have shown that the proposed scheme improves QoE compared to the existing quality control schemes by minimizing the buffer underflow and the unnecessary quality changes and maximizing the average video quality.

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Recently, a couple of analytical models for video streaming have been proposed, allowing to compare these input parameters and derive their impact on QoE-relevant metrics for HAS-based video delivery. The outcome of these models are typically asymptotic probabilities, distribution functions, or centralized and standardized moments. For instance, these models do not yield any temporal information in terms of stalling events or requested video quality. This contradicts to QoE prediction models like P.1203, which compute the QoE based on the chronological sequence of a specific video playback. So far, it is unclear how and to which extent the generalized results of analytical models can be utilized to derive sequence-based QoE values or the QoE distribution for a set of sequences for similar input parameters with stochastic variations. To address this problem, we compare testbed measurements with the output of a GI/GI/1 model with pq-policy and buffer-based quality switching capability to conclude to which extent the results still allow to approximate the video QoE.

Scalable High efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard High Efficiency Video Coding (HEVC). One of the key novelties introduced by SHVC is that it enables hybrid codec scalability. This basically means that the video layers can be encoded with different video standards providing backward compatibility between codecs. In this paper, we propose a software parallel SHVC decoder in hybrid codec scalability configuration. The proposed design consists of an Advanced Video Coding (AVC) decoder for the Base Layer (BL) and a HEVC decoder for the Enhanced Layer (EL). In order to perform Inter Layer Prediction (ILP), a communication of decoding states and outputs is established between the two decoders. While the native frame based parallelism is still allowed within the two decoders, the proposed design also enables the use of frame based parallelism between the two decoders. The proposed software design enables a real time decoding of the HEVC EL at 2160p60 while the AVC base layer is decoded at 1080p60 for ×2 spatial scalability.

Current digital video applications require delivery of digital video content to different clients over heterogeneous networks. These clients may have different system resources and bandwidth capabilities. Thus, delivering of digital video data compressed at specific spatial resolution, quality levels, frame rates and encoding those video data at specific bit rates according to the client resources and the available network is essential. The characteristics of current video transmission system pose many problems for video delivery and scalable video coding is a highly attractive solution. This thesis investigates the existing video coding standards, especially the newest H.264/advanced video coding and its scalable extension: scalable video coding. In this thesis, we address two issues related to the state-of-the-art scalable video coding: 1) computational complexity reduction; and 2) video coding efficiency enhancement. In the first part of this thesis, we address the complexity issue of spatial, signal-to-noise ratio (SNR), temporal and combined scalable video coding. Our goal is to find a good tradeoff between computational complexity and video coding efficiency. We first establish the need to reduce computational complexity for current H.264/advanced video coding and scalable video coding framework. We then evaluate the mode distribution correlations between the base layer and its enhancement layers for different video scalabilities. After the exhaustive search over all possible block partitions is performed at the base layer, the number of candidate modes for luma and chroma blocks in a macroblock that take part in rate distortion optimization calculation at enhancement layers could be greatly reduced based on the correlations. Finally, adaptive fast mode decision schemes for spatial, SNR and temporal scalable video coding are presented. Our schemes could achieve consistent and significant computational complexity reduction with negligible loss in objective and subjective video quality and insignificant increments in bit rate consumption. The second part of this thesis is to provide a solution for the problem of high computational complexity of scalable video decoders. An adaptive encoding algorithm is proposed to reduce decoder complexity in coarse grain SNR scalable video coding.

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264#x002F;advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.

A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.

With the rapid development of Information and Communication Technology, mobile HTTP adaptive streaming (HAS) technologies have gained popularity. Thusly, the ability to evaluate users' Quality of Experience (QoE) is important. However, changing the transmitted video quality as employed by HAS poses more psychological impact like memory effect on subjectively perceived video quality and modeling HAS QoE considering memory effect is still a challenge. In this paper, we propose a QoE model for short-form HAS video considering memory effect. In order to assess the effect that each segment has in the overall video quality, a subjective online test based on the wisdom of the crowds is conducted. The result shows primacy effect is more obvious than recency effect in short-form videos. The proposed method is validated by plenty subjective tests and the simulation results show that our QoE model for short-form video is powerful.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for Over-The-Top video streaming. A HAS video consists of multiple segments, encoded at multiple quality levels. Allowing the client to select the quality level for every segment, a smoother playback and a higher Quality of Experience (QoE) can be perceived. Although results are promising, current quality selection heuristics are generally hard coded. Fixed parameter values are used to provide an acceptable QoE under all circumstances, resulting in suboptimal solutions. Furthermore, many commercial HAS implementations focus on a video-on-demand scenario, where a large buffer size is used to avoid play-out freezes. When the focus is on a live TV scenario however, a low buffer size is typically preferred, as the video play-out delay should be as low as possible. Hard coded implementations using a fixed buffer size are not capable of dealing with both scenarios. In this paper, the concept of reinforcement learning is introduced at client side, allowing to adaptively change the parameter configuration for existing rate adaptation heuristics. Bandwidth characteristics are taken into account in the decision process, thus allowing to improve the client's bandwidth-awareness. Focus in this paper is on actively reducing the average buffer filling, evaluating results for two heuristics: the Microsoft IIS Smooth Streaming heuristic and the QoE-driven Rate Adaptation Heuristic for Adaptive video Streaming by Petrangeli et al. We show that using the proposed learning-based approach, the average buffer filling can be reduced by 8.3% compared to state of the art, while achieving a comparable level of QoE.

Lately, HTTP Adaptive Streaming (HAS) has become dominant among different video streaming techniques. However, owing to the unpredictable nature of the wireless radio channel and device mobility, using HAS over mobile wireless networks is still very challenging. In this paper, we propose a novel Quality of Experience (QoE) optimization mechanism for HAS in the context of mobile wireless networks. The proposed mechanism leverages recent advances in HAS specification, which includes new features for QoE measurements and reporting. First, we formulate a discrete optimization problem aiming at maximizing the overall average quality, and minimizing the negative impact of temporal video quality changes for all HAS users simultaneously. Second, in order to take advantage of well-known continuous optimization techniques and to decrease the computational complexity, we convert the formulated problem into a continuous form, and propose a gradient based algorithm to solve the continuous optimization problem. The results of our simulations demonstrate that our system attained better perceived video quality by almost 8% on average, while lowering the freezing period by 20% on average across HAS users when compared to other approaches where HAS users only rely on local adaptation logics.

H.264 Scalable Video Coding (SVC) is an extension to the Advanced Video Coding (AVC) H.264 standard which provides efficient scalability functionalities on top of the high coding efficiency of H.264/AVC. SVC allows for temporal, spatial, and quality scalability of the output video stream, encoding the video information into an H.264/AVC base layer and a series of enhancement layers which incrementally improve the quality, increase screen resolution and/or frame rate. SVC is particularly suited for mobile TV reception, since the received video quality is adaptable to variable reception conditions and heterogeneous receiver capabilities. However, mobile TV Digital Video Broadcasting (DVB) standards such as DVB-H and DVB-SH were designed prior to the introduction of SVC, and therefore the underlying transmission protocols are not optimized for scalable video delivery. In this overview paper, we review recently proposed solutions for SVC stream adaptation on the underlying DVB-H/SH protocols, and point out novel technical solutions that are currently under consideration for the next generation mobile broadcasting standard DVB-NGH.

Predictive green streaming have recently gained attention in wireless network literature due to its significant energy-savings and quality of experience (QoE) gains. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular Hypertext Transfer Protocol (HTTP) [e.g., HTTP adaptive streaming (HAS)]. To this end, we develop a stochastic predictive HTTP Adaptive streaming (PHAS) optimization framework to achieve the following objectives: 1) an edge-cloud assisted framework for prediction based HAS and identify its key functional entities and their interactions; 2) modelling uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach which dynamically allocate the risks and optimize system efficiency over a time horizon; 3) an efficient heuristic algorithm allocating time slot ratio for multi-users to improve the network efficiency, fairness and overall QoE under different prediction error variances, wireless link conditions and buffer length constraints; Simulation studies and analytical results show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.