QoE optimization for HTTP adaptive streaming: Performance evaluation of MEC-assisted and client-based methods

Seamless streaming of high quality video under unstable network condition is a big challenge. HTTP adaptive streaming (HAS) provides a solution that adapts the video quality according to the network conditions. Traditionally, HAS algorithm runs at the client side while the clients are unaware of bottlenecks in the radio channel and competing clients. The traditional adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. The lack of coordination has been shown to lead to suboptimal user experience. As a response, multi-access edge computing (MEC)-assisted adaptation techniques emerged to take advantage of computing and content storage capabilities in mobile networks. In this study, we investigate the performance of both MEC-assisted and client-side adaptation methods in a multi-client cellular environment. Evaluation and comparison are performed in terms of not only the video rate and dynamics of the playback buffer but also the fairness and bandwidth utilization. We conduct extensive experiments to evaluate the algorithms under varying client, server, dataset, and network settings. Results demonstrate that the MEC-assisted algorithms improve fairness and bandwidth utilization compared to the client-based algorithms for most settings. They also reveal that the buffer-based algorithms achieve significant quality of experience; however, these algorithms perform poorly compared with throughput-based algorithms in protecting the playback buffer under rapidly varying bandwidth fluctuations. In addition, we observe that the preparation of the representation sets affects the performance of the algorithms, as does the playback buffer size and segment duration. Finally, we provide suggestions based on the behaviors of the algorithms in a multi-client environment.     

TRASS: A transmission rate‐adapted streaming server in a wireless environment

The video streaming quality in a wireless communication network environment is largely affected by various network characteristics, such as a limited channel bandwidth and a variant transmission rate. The playback quality of User Equipments (UEs) may not be smooth when the service is delivered via a wireless environment. From the viewpoints of most video receivers, a smooth playback with a lower video quality may be more significant than a lagged or distorted playback with a higher video quality as the transmission rate degrades. Based on the above, we sketch an adaptation agent—Transmission‐Rate Adapted Streaming Server (TRASS), which is located between the original video server and UEs, to adaptively transform the streaming video based on the real transmission rate. In our proposed scheme, UEs would feedback their network access statuses to TRASS and then TRASS would deliver adaptive quality of video streams to UEs according to their feedbacks. The theoretical analysis and simulations using different video tracks encoded in MPEG‐4 and H.264/AVC formats show that TRASS can help wireless streaming users to get a smooth playback quality with a lower packet failure rate. With a low probability of receiving a worse quality of video, users' Quality of Experience can subsequently be raised. Copyright © 2010 John Wiley & Sons, Ltd.     

Video transcoding for adaptive bitrate streaming over edge-cloud continuum

Video transcoding is to create multiple representations of a video for content adaptation. It is deemed as a core technique in Adaptive BitRate (ABR) streaming. How to manage video transcoding affects the performance of ABR streaming in various aspects, including operational cost, streaming delays, Quality of Experience (QoE), etc. Therefore, the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services. These problems become more worthy of investigation with the emergence of the edge-cloud continuum, which makes the resource allocation for video transcoding more complicated. To this end, this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming, including designing a rate profile for video transcoding, providing resources for video transcoding in clouds, and caching multi-bitrate video contents in networks, etc. We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service (QoS) optimization problems. The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming. We also discuss some promising research directions for the ABR streaming services.     

Eliminating bandwidth estimation from adaptive video streaming in wireless networks

Dynamic Adaptive Streaming over HTTP (DASH) is the state-of-the-art technology for video streaming and has been widely deployed in both wired and wireless environments. However, mobile DASH users often suffer from video quality oscillation and even video freeze in wireless environments, which results in poor user experience. This is mainly because most quality adaptation algorithms in DASH rely highly on bandwidth estimation to adjust the video quality while wireless network bandwidth is unstable in nature and changes frequently according to wireless channel contention and condition. To provide stable performance, even during severe bandwidth fluctuation, this paper proposes the Wireless Quality Adaptation (WQUAD) algorithm, which eliminates bandwidth estimation from quality adaptation. Thanks to the Scalable Video Codec (SVC), the proposed scheme always prioritizes to lower layers over higher ones as long as the play-out buffer is not completely filled by the lower layers. As a result, the client always fills the buffer with the base layers first and then the upper enhancement layers sequentially. This horizontal adaptation is straightforward and does not require any bandwidth estimation. Through NS-2 simulations, we show that WQUAD achieves (i) stable performance, keeping the video quality level with respect to the long-term network bandwidth, (ii) effective video freeze prevention, and (iii) high video quality on average.     

A brief survey on adaptive video streaming quality assessment

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. Through the performance analysis, we observe that hybrid models perform better than both quality-of-service (QoS) driven QoE approaches and signal fidelity measurement. Moreover, the machine learning-based model slightly outperforms the model without using machine learning for the same setting. In addition, we find that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems. Therefore, based on the success of deep learned feature representations for traditional video quality prediction, we also apply the off-the-shelf deep convolutional neural network (DCNN) to evaluate the perceptual quality of streaming videos, where the spatio-temporal properties of streaming videos are taken into consideration. Experiments demonstrate its superiority, which sheds light on the future development of specifically designed deep learning frameworks for adaptive video streaming quality assessment. We believe this survey can serve as a guideline for QoE assessment of adaptive video streaming.     

Research on Q-learning based rate control approach for HTTP adaptive streaming

HTTP adaptive streaming (HAS) has become the standard for adaptive video streaming service.In changing network environments,current hardcoded-based rate adaptation algorithm was less flexible,and it is insufficient to consider the quality of experience (QoE).To optimize the QoE of users,a rate control approach based on Q-learning strategy was proposed.the client environments of HTTP adaptive video streaming was modeled and the state transition rule was defined.Three parameters related to QoE were quantified and a novel reward function was constructed.The experiments were employed by the Q-learning rate control approach in two typical HAS algorithms.The experiments show the rate control approach can enhance the stability of rate switching in HAS clients.     

Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network

Recently the 3rd Generation Partnership Project (3GPP) and the Moving Picture Experts Group (MPEG) specified Dynamic Adaptive Streaming over HTTP (DASH) to cope with the shortages in progressive HTTP based downloading and Real-time Transport Protocol (RTP) over the User Datagram Protocol (UDP), shortly RTP/UDP, based streaming. This paper investigates rate adaptation for the serial segment fetching method and the parallel segment fetching method in Content Distribution Network (CDN). The serial segment fetching method requests and receives segments sequentially whereas the parallel segment fetching method requests media segments in parallel. First, a novel rate adaptation metric is presented in this paper, which is the ratio of the expected segment fetch time (ESFT) and the measured segment fetch time to detect network congestion and spare network capacity quickly. ESFT represents the optimum segment fetch time determined by the media segment duration multiplied by the number of parallel HTTP threads to deliver media segments and the remaining duration to fetch the next segment to keep a certain amount of media time in the client buffer. Second, two novel rate adaptation algorithms are proposed for the serial and the parallel segment fetching methods, respectively, based on the proposed rate adaptation metric. The proposed rate adaptation algorithms use a step-wise switch-up and a multi-step switch-down strategy upon detecting the spare networks capacity and congestion with the proposed rate adaptation metric. To provide a good convergence in the representation level for DASH in CDN, a sliding window is used to measure the latest multiple rate adaptation metrics to determine switch-up. To decide switch-down, a rate adaptation metric is used. Each rate adaptation metric represents a reception of a segment/portion of a segment, which can be fetched from the different edge servers in CDN, hence it can be used to estimate the corresponding edge server bandwidth. To avoid buffer overflow due to a slight mismatch in the optimum representation level and bandwidth, an idling method is used to idle a given duration before sending the next segment. In order to solve the fairness between different clients who compete for bandwidth, the prioritized optimum segment fetch time is assigned to the newly joined clients. The proposed rate adaptation method does not require any transport layer information, which is not available at the application layer without cross layer communication. Simulation results show that the proposed rate adaptation algorithms for the serial and the parallel segment fetching methods quickly adapt the media bitrate to match the end-to-end network capacity, provide an advanced convergence and fairness between different clients and also effectively control buffer underflow and overflow for DASH in CDN. The reported simulation results demonstrate that the parallel rate adaptation outperforms the serial DASH rate adaptation algorithm with respect to achievable media bitrates while the serial rate adaptation is superior to the parallel DASH with respect to the convergence and buffer underflow frequency.     

一个基于速率控制的Internet视频流服务方案

由于视频流服务对于网络服务质量有着较高的要求,而现有的Internet所提供的是尽力而为的服务,无法保证数据的实时传输。该文设计了一个用于Internet上视频流的端到端传输方案.整个方案设计的目的是在网络本身缺乏服务质量保证的条件下尽可能达到最好的视频传输质量。根据可用带宽估计和网络信息反馈,系统对发送速率进行调整,并提供两种视频流服务:存储视频和实时视频。仿真结果表明方案的性能良好,能满足Internet视频流的需求。     

MDVQM: A novel multidimensional no-reference video quality metric for video transcoding

In this paper, we study the impact of quantization, frame dropping and spatial down-sampling on the perceived quality of compressed video streams. Based on the analysis of quality ratings obtained from extensive subjective tests, we propose a no-reference metric (named MDVQM) for video quality estimation in the presence of both spatial and temporal quality impairments. The proposed metric is based on the per-pixel bitrate of the encoded stream and selected spatial and temporal activity measures extracted from the video content. All the values required to compute the proposed video quality metric can be obtained without using the original reference video which makes the metric for instance useful for making transcoding decisions in a wireless video transmission scenario. Different from comparable metrics in the literature, we have also considered the case when both frame rate and frame size are changed simultaneously. The validation results show that the proposed metric provides more accurate estimation of the video quality than the state of the art metrics.     

基于OpenFlow的SVC流媒体时延自适应分级传输方法

针对当前互联网流媒体传输的时延敏感性问题,提出一种基于OpenFlow的SVC（scalable video coding,可分级视频编码）流媒体时延自适应分级传输方法,该方法有效结合SVC流媒体可分级和OpenFlow灵活可编程的特性,在网络带宽受限和链路拥塞的复杂网络环境下,通过构建基础层和增强层2个独立路由,实现了动态网络下SVC流媒体分级自适应高效传输。仿真结果表明,该方法在提升SVC流媒体传输效率和质量,改善用户体验方面有重要作用。     

一种信道自适应的无线视频流差错控制机制

提出了一种自适应的无线视频流差错保护方法。该方法充分考虑MPEG-4FGS码流细粒度可扩展的特性,根据码流的重要性程度采用不等的保护措施,同时能够自适应地根据无线信道的具体状况,将信道带宽在MPEG-4FGS信源编码速率和信道编码速率之间进行最优化分配,使得接收端能获得最佳的重建视频质量。实验结果表明,在各种不同信道状况下,与均等错误保护和固定的不等错误保护方法相比,该方法均可获得更好的性能。     

Hierarchical modulation for client-driven selective streaming of multi-view video over AWGN channels

We consider a client-driven selective streaming system for multi-view video, which is supported by scalable multi-view video coding (SMVC). The system is used to reduce bit-rates for efficient transmission of multi-view video. The transmit source is partitioned into three layers: a base and two enhancement layers. The base layer contains all views encoded by MVC at a low-quality, while each enhancement layer contains high-quality part of the selected left and right views, respectively. The base layer is more important than the enhancement layers to create 3D perception, albeit of low-quality, and should be more protected than the enhancement layers. In this paper, two-level and three-level hierarchical 64QAM are used to provide unequal error protection (UEP) for the client-driven selective streaming system. First we find the suitable hierarchical values, which is the ratio of the distances in hierarchical 64QAM, for the client-driven selective streaming system. Furthermore, we analyze numerically how the hierarchical 64QAM impacts peak signal-to-noise ratio (PSNR) performance of fast and slow-motion sequences. Simulation results show that the three-level hierarchical 64QAM outperforms both conventional 64QAM with Gray mapping and the two-level hierarchical 64QAM in terms of achieving the target average PSNR performance at a low SNR environment.     

Tile caching for scalable VR video streaming over 5G mobile networks

Currently, VR video delivery over 5G systems is still a very complicated endeavor. One of the major challenges for VR video streaming is the expectations for low latency that current mobile networks can hardly meet. Network caching can reduce the content delivery latency efficiently. However, current caching schemes cannot obtain ideal results for VR video since it requests the viewport interactively. In this paper, we propose a tiled scalable VR video caching scheme over 5G networks. VR chunks are first encoded into multi-granularity quality layers, and are then partitioned into tiles to facilitate viewport data access. By accommodating the 5G network infrastructure, the tiles are cooperatively cached in a three-level hierarchal system to reduce delivery latency. Furthermore, a quality-adaptive request routing algorithm is designed to cater for the 5G bandwidth dynamics. Experimental results show that the proposed scheme can reduce the transmission latency over conventional constant bitrate video caching schemes.     

QoS-supporting video streaming system with minimum data service cost over heterogeneous wireless networks

This paper presents a video streaming system that supports quality-of-service by effectively consolidating multiple physical paths in a cost-effective way over heterogeneous wireless networks. In the proposed system, the fountain encoding symbols of compressed video data are transmitted through multiple physical paths concurrently to overcome the limitation of single path transmission and harmonize multiple physical paths with diverse characteristics effectively, and the number of transmitted packets is determined by considering the requested quality-of-service of video streaming and the data service cost. The proposed system is fully implemented in Java and C/C++, and tested over real WLAN and LTE networks. Experimental results are provided to demonstrate the performance improvement of the proposed system.     

Quality perception when streaming video on tablet devices

The proposed work aims at analyzing the quality perceived by the user when streaming video on tablet devices. The contributions of this paper are: (i) to analyze the results of subjective quality assessments to determine which Quality of Service (QoS) parameters mainly affect the users’ Quality of Experience (QoE) in video streaming over tablet devices; (ii) to define a parametric quality model useful in system control and optimization for the considered scenarios; (iii) to compare the performance of the proposed model with subjective quality results obtained in alternative state-of-the-art studies and investigate whether other models could be applied to our case and vice versa.     

3D visual experience oriented cross-layer optimized scalable texture plus depth based 3D video streaming over wireless networks

3D video streaming over the mobile Internet generally incurs the inferior 3D visual experience due to the time-varying characteristics of wireless channel. The conventional video streaming optimization methods generally neglect the harmony among different networking protocol layers. This paper proposes a cross-layer optimized texture plus depth based scalable 3D video streaming method to improve the expected 3D visual experience of the user by systematically considering the application layer texture-video/depth/FEC bit-rate allocation, MAC layer multi-channel allocation, and physical layer modulation and channel coding scheme (MCS) selection. In the cross-layer optimization, a networking-related 3D visual experience model which fuses the overlapped retinal view visual quality and depth sensation with mimicking human vision system is established to predict the 3D visual experience under the specific parameter configurations of different protocol layers. The efficiency and effectiveness of the proposed cross-layer optimized 3D video streaming method has been validated by subjective and objective experimental results.     

A distributed utility‐based scheduling for peer‐to‐peer video streaming over wireless networks

Interactive multimedia applications such as peer‐to‐peer (P2P) video services over the Internet have gained increasing popularity during the past few years. However, the adopted Internet‐based P2P overlay network architecture hides the underlying network topology, assuming that channel quality is always in perfect condition. Because of the time‐varying nature of wireless channels, this hardly meets the user‐perceived video quality requirement when used in wireless environments. Considering the tightly coupled relationship between P2P overlay networks and the underlying networks, we propose a distributed utility‐based scheduling algorithm on the basis of a quality‐driven cross‐layer design framework to jointly optimize the parameters of different network layers to achieve highly improved video quality for P2P video streaming services in wireless networks. In this paper, the quality‐driven P2P scheduling algorithm is formulated into a distributed utility‐based distortion‐delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Specifically, encoding behaviors, network congestion, Automatic Repeat Request/Query (ARQ), and modulation and coding are jointly considered. Then, we provide the algorithmic solution to the formulated problem. The distributed optimization running on each peer node adopted in the proposed scheduling algorithm greatly reduces the computational intensity. Extensive experimental results also demonstrate 4–14 dB quality enhancement in terms of peak signal‐to‐noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.