A QoE centric distributed caching approach for vehicular video streaming in cellular networks

Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.     

基于A3C的无线异构网络自适应视频流传输控制方法

比特率自适应（ABR）算法已经成为视频传输中研究的热点之一。然而,由于5G无线异构网络具有信道带宽波动大、不同网络间差异明显等特点,多终端协同的自适应视频流传输面临着巨大挑战。提出了一种基于深度强化学习的自适应视频流传输控制方法。首先,建立了视频流动态规划模型,对传输码率以及分流策略进行联合优化。由于该优化问题的求解依赖于精确的信道估计,这在信道状态动态变化的网络中很难实现。因此,将动态规划问题改进为强化学习任务,并采用A3C算法,动态决策视频码率和分流策略。最后,根据实测的网络数据进行仿真,与传统的优化方法相比,本文所提的方法较好地提高了用户QoE。     

Adaptive video streaming testbed design for performance study and assessment of QoE

Hypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high‐oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end‐user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real‐life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions. In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.     

A QoE management system to improve the IPTV network

One of the biggest problems of the IPTV providers is to offer enough quality of experience (QoE) to their customers. The minimum bandwidth in the access network required to provide IPTV services, jointly with the necessity to guarantee the QoE to the customers, creates the need for new type of algorithms to satisfy the network requirements. Apart from the parameters that depend on the application aspect and design, the user's QoE mainly depends on the video quality (VQ), which can be affected by the network parameters (jitter, delay, lost packets, etc.), and on other parameters, such as zapping time and synchronization time. In this paper, we implement several benches to know how each measurable parameter affects the user's QoE. Taking into account these parameters, we propose an analytical expression for the QoE calculation. Then, the paper presents a network management algorithm that takes into account the information received by the user to take the appropriate actions and vary some features of the IP network to provide enough IPTV QoE to the customer. Finally, we measure how one of the actions taken by our system affects the network performance and the VQ. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

HTTP自适应流媒体直播系统中的用户体验质量优化

分析HTTP自适应流媒体直播系统中对终端用户体验质量(QoE)产生影响的各类因素及其相互之间的作用关系,对基于服务器端、网络传输以及客户端的QoE优化策略进行总结。认为HTTP自适应流媒体直播系统的QoE优化重点在于降低延时,提出结合网络层和应用层影响因素来降低时延并提升用户QoE的建议。     

Link efficiency and quality of experience aware routing protocol to improve video streaming in urban VANETs

In a vehicular ad‐hoc network (VANET), vehicles can play an essential role in monitoring areas of a smart city by transmitting data or multimedia content of environmental circumstances like disasters or road conditions. Multimedia content communication with quality of experience (QoE) guarantees is a challenging undertaking in an environment such as that of a VANET. Indeed, a VANET is characterized by numerous varying conditions, significantly impacting its topology, quality of communication channels, and paths with respect to bandwidth, loss, and delay. This paper introduces a link efficiency and quality of experience aware routing protocol (LEQRV) to improve video streaming provisioning in urban vehicular ad‐hoc networks. LEQRV uses an enhanced greedy forwarding‐based approach to create and maintain stable high quality routes for video streaming delivery. It improves the performance of the quality of experience by increasing the achieved QoE scores and reducing the forwarding end‐to‐end delay and frame loss.     

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.     

基于TCP协议的音频流媒体QoE的定量研究

流媒体接收端音频的初始延时或中断,以及采用自适应播放产生的播放速率变化是基于TCP协议的音频流媒体的用户体验质量下降的主要原因。介绍了一种中断强度的概念,并通过模拟实际网络环境的主观音质测试方法来分析中断强度对QoE的影响。结果表明,在可用带宽发生变化时,中断强度和主观音质测试得分有很好的相关性,平均相关系数为-0.9。从而表明中断强度可以作为评价音频流媒体QoE的客观评价参数。此外,还对播放速率变化对QoE的影响进行了主观音质测试,变速算法为波形相似叠加算法。测试结果显示,当变速因子超过105%时,音质下降严重,测试结果可以作为自适应播放中参数设置的参考。     

基于强化学习的立体全景视频自适应流

针对当前立体全景视频传输缺少有效的流自适应方法,且传统全景视频流自适应策略传输双目立体全景视频使得传输数据加倍,所需带宽巨大的问题,该文提出一种基于多智能体强化学习的立体全景视频非对称传输自适应流方法,以实时应对网络带宽波动.首先,根据人眼对视频显著性区域的偏爱,左右视点中每个瓦片(tile)对立体视频的感知质量的贡献...     

On quality of experience of scalable video adaptation

In this paper, we study the quality of experience (QoE) issues in scalable video coding (SVC) for its adaptation in video communications. A QoE assessment database is developed according to SVC scalabilities. Based on the subjective evaluation results, we derive the optimal scalability adaptation track for the individual video and further summarize common scalability adaptation tracks for videos according to their spatial information (SI) and temporal information (TI). Based on the summarized adaptation tracks, we conclude some general guidelines for the effective SVC video adaptation. A rate-QoE model for SVC adaptation is derived accordingly. Experimental results show that the proposed QoE-aware scalability adaptation scheme significantly outperforms the conventional adaptation schemes in terms of QoE. Moreover, the proposed QoE model reflects the rate and QoE relationship in SVC adaptation and thus, provides a useful methodology to estimate video QoE which is important for QoE-aware scalable video streaming.     

Statistical Analysis of a Subjective QoE Assessment for VVoIP Applications

A successful deployment of multimedia applications over wireless environments entails improving the quality of service (QoS), not only from a technical point of view, but also considering the quality of experience (QoE) from the final user's perception. Although objective QoE measure models avoid the difficulties of subjective surveys, subjective QoE assessments are essential to understand the way users evaluate the QoS. In this work, we study the effect of a wide range of parameters on the QoE of VVoIP applications in a real wireless scenario. Through a complete statistical analysis of users’ ratings, we identify the following facts. Although the use of VVoIP in wireless networks does not yet represent an advantage for users, there are great expectations for all applications under study, and with greater popularity comes higher expectations. It is easier for respondents to identify good behavior than poor behavior. Whereas the respondents’ frequency of Internet use does not impact on the scores, respondents’ gender does. Finally, the most determining parameters of quality from a user's perspective were instability, video quality, voice distortion, usefulness, and graphical interface.     

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.     

SeekStream: adapting to dynamic user behavior in P2P video‐on‐demand

In recent years, the users' perceived quality of experience (QoE) in streaming services has gained a lot of attention. Particularly, a number of research efforts have focused on providing live streaming and video‐on‐demand (VoD) services using peer‐to‐peer (P2P) architectures. However, in these proposed architectures, the heterogeneity of users and their dynamic behavior has not been sufficiently studied. In a real life scenario, where users have highly heterogeneous bandwidth resources (cable, DSL, 3G networks, etc) and can arbitrarily decide to perform a VCR function (stop, fast forward and seeking), ignoring this behavior can significantly deteriorate the system's efficiency and the perceived QoE. In this paper, we present SeekStream, a scalable P2P VoD architecture that ensures the stable delivery of the video stream to every participating user even in cases of high heterogeneity and frequent seeking operations. Specifically, SeekStream is a set of algorithms that optimize the P2P overlay dynamically and in a distributed fashion, making it adaptive to users dynamic behavior and bandwidth changes. The available bandwidth resources of the participating users are optimally exploited, keeping the contribution from the media server(s) to a minimal level. To illustrate the performance of the proposed algorithms, we are using a centralized overlay network manager that discovers the optimal network graph as a reference. We have developed an extensive P2P VoD simulator that shows the efficiency, scalability, and stability of our system under variant and dynamic conditions. The algorithms of our proposed system introduce less than 4% bandwidth overhead while we achieve high offloading of the media server(s). SeekStream guarantees a high block reception rate for the users, even under extreme seeking patterns. Copyright © 2016 John Wiley & Sons, Ltd.     

Impact of playout buffer dynamics on the QoE of wireless adaptive HTTP progressive video

The quality of experience (QoE) of video streaming is degraded by playback interruptions, which can be mitigated by the playout buffers of end users. To analyze the impact of playout buffer dynamics on the QoE of wireless adaptive hypertext transfer protocol (HTTP) progressive video, we model the playout buffer as a G/D/1 queue with an arbitrary packet arrival rate and deterministic service time. Because all video packets within a block must be available in the playout buffer before that block is decoded, playback interruption can occur even when the playout buffer is non-empty. We analyze the queue length evolution of the playout buffer using diffusion approximation. Closed-form expressions for user-perceived video quality are derived in terms of the buffering delay, playback duration, and interruption probability for an infinite buffer size, the packet loss probability and re-buffering probability for a finite buffer size. Simulation results verify our theoretical analysis and reveal that the impact of playout buffer dynamics on QoE is content dependent, which can contribute to the design of QoE-driven wireless adaptive HTTP progressive video management.     

移动网络中HTTP视频流终端数据分析及应用

伴随移动通信技术的发展,越来越多的用户选择通过移动网络随时随地观看网络视频,移动视频业务迅速增长.在现有的无线视频传输方式中,由于TCP的可靠性以及易穿透防火墙和网络地址转化等优点,基于TCP的HTTP流逐渐成为网络视频的主流传榆方式.另一方面,用户对业务体验需求的上升,也给移动数据的传输带来巨大挑战.该文通过采集和分析HTTP流的终端数据,获到了HTTP流在HSPA+中传输特性及关键参数的特征,分析结果表明其视频块传输速率变化与自回归模型近似,并在此基础上,提出一种视频等级自适应控制算法,能够有效地改善用户体验.     

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.     

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.     

对主流自适应码率流媒体技术的分析

伴随着互联网多媒体应用的快速增长,尤其是在线视频业务的不断发展,传统的流媒体技术由于其单一的编码方式难以适应差异化的用户网络变化,极大地影响了用户体验。分析了当前在线视频业务面临的主要问题,同时介绍了新兴的自适应码率流媒体技术的特点和优势。通过对主流自适应码率流媒体和传统流媒体技术的分析和比较,表明对于OTT业务而言,标准化的自适应码率流媒体技术比传统的流媒体技术更加有优势。