Design and Implementation of a Network‐Adaptive Mechanism for HTTP Video Streaming

This paper proposes a network‐adaptive mechanism for HTTP‐based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time‐variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.     

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.     

面向智能网联汽车边缘网络的分布式端-边协同算法

车联网高级安全服务中,智能网联车辆配备了摄像头,可以拍摄周围的视频,用于安全、交通监控和监视等目的。车辆将获取的视频上传到边缘计算节点后,可以对视频进行分析和备份,以满足不同的安全驾驶需求。然而,车辆连续直接向边缘计算节点上传生成的视频内容会非常消耗带宽,并消耗大量的能量。基于该问题,提出一种面向智能网联汽车边缘网络的分布式端-边协同算法。针对车联网高可靠低时延内容传输的特点,引入有限块长度编码机制。同时,引入车辆视频信息源的压缩编码功率消耗,建立车辆能耗模型。根据车辆视频信息源的视频质量要求,通过调整视频编码码率、信息源传输速率,以及车辆多路径路由的决策,提出一种完全分布式的优化算法,以提高网络资源利用率,并保证单个车辆的能耗公平性。     

Modeling and performance analysis of dynamic spectrum sharing between DSRC and Wi‐Fi systems

The Notice of Proposed Rulemaking 13‐22 released by Federal Communications Commission unlocks the Dedicated Short Range Communication (DSRC) spectrum for Wi‐Fi availability, which undoubtedly brings unpredictable effects to the new‐emerging vehicular applications and services. To efficiently harmonize the spectrum operation between DSRC and Wi‐Fi networks, several dynamic spectrum‐sharing schemes are already proposed to improve the spectral efficiency over a limited bandwidth situation and as well to satisfy the ever‐increasing demand for bandwidth resource. Different from most previous literature that mainly focused on the performance analysis of cellular‐network‐centric spectrum sharing, we aim to analyze the performance of the mainstream dynamic spectrum‐sharing schemes specially designed for the coexistence of DSRC and Wi‐Fi networks against various combinations of network parameters through a hybrid network model and performance indicators. We employ the Poisson point process to model a hybrid network where DSRC vehicles and Wi‐Fi devices coexist, and introduce the performance indicators of spectrum efficiency and data rate to assess the utility of different spectrum sharing candidates. Through the presented hybrid model and performance indicators, we collect extensive numerical and simulation results to investigate four typical spectrum allocation schemes for DSRC and Wi‐Fi coexistence, that is non‐sharing scheme, original sharing scheme, and Qualcomm's and Cisco's proposals, respectively. The results show that the dynamic spectrum sharing in the 5.9‐GHz band can significantly raise the performance of Wi‐Fi network without excessively degrading the DSRC system, and especially the Cisco's proposal prefers to protect the DSRC profit while the Qualcomm's draft favors Wi‐Fi exclusively. Copyright © 2016 John Wiley & Sons, Ltd.     

Fuzzy‐based trust prediction for effective coordination in vehicular ad hoc networks

In a vehicular ad hoc network, trust between vehicles is vital to efficiently transmit the data among vehicles. Secure node selection is based on the trust value that a node can calculate during its operations in a dynamic network. This will increase the confidentiality level of the network, thus improving the network performance. However, defining confidentiality is not a crisp task, rather it can range between certain limits. A fuzzy‐based inference engine can be used to optimally define these limits. In this paper, a fuzzy‐based trust prediction model is proposed to effectively compute the trust of other vehicles for the secure path formation in vehicular ad hoc networks. In the designed approach, each vehicle models the trustworthiness of the neighbors in its cognitive range to dynamically select the relay nodes that can be used for data transmission and the election of the appropriate path for routing in vehicular ad hoc network environment. The results and analysis of the proposed model over the standard protocols are presented using simulations. Copyright © 2016 John Wiley & Sons, Ltd.     

An efficient adaptive intelligent routing system for multi‐intersections

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car‐following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections. Copyright © 2016 John Wiley & Sons, Ltd.     

A traffic-camera assisted cache-and-relay routing for live video stream delivery in vehicular ad hoc networks

With the development of the Internet of Things, installation of smart mobile terminal in vehicle has become more and more popular, and consequently, how to provide services for the public utilizing vehicular ad hoc networks has aroused great interest in research and industrial areas. Among them, the kind of services supported by live video streaming attracts more attention because of its advantages. However, due to the high vehicular speed, frequent disconnection and dynamic topology, it’s difficult to guarantee the low-delay delivery of real-time video data, and there is hardly any scheme that can deliver such kind of data with satisfied quality. In this paper, we take fully advantage of the existing wireless enabled traffic cameras, and propose a novel traffic-camera assisted routing for video delivery, which can deliver live video stream to mobile target vehicle with minimal start-up delay while satisfying the required visual quality and playback performance through optimal buffering points selection and intelligent relay among them. The trace-driven simulations demonstrate that our strategy outperforms existing solutions greatly.     

Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

HTTP adaptive streaming (HAS) is becoming the de facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. It has been shown that state‐of‐the‐art heuristics perform suboptimal when sudden bandwidth drops occur, therefore leading to freezes in the video playout, the main factor influencing users' quality of experience (QoE). This issue is aggravated in case of live events, where the client‐side buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In this article, we propose a framework capable of increasing the QoE of HAS clients by reducing video freezes. The framework is based on OpenFlow, a widely adopted protocol to implement the software‐defined networking principle. An OpenFlow controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and the HAS clients' status. Particularly, the HAS clients' status is obtained without any explicit clients‐to‐controller communication, and thus, no extra signaling is introduced into the network. Moreover, this OpenFlow controller is transparent to the quality decision process of the clients, as it assists the delivery of the segments, but it does not determine the actual quality to be requested. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of the proposed OpenFlow‐based framework in the presence of realistic Internet cross‐traffic. Particularly, we model two types of applications, namely, HTTP web browsing and progressive download video streaming, which currently represent the majority of Internet traffic together with HAS. By evaluating this novel approach through emulation in several multi‐client scenarios, we show how the proposed approach can reduce freeze time for the HAS clients due to network congestion up to 10 times compared with state‐of‐the‐art heuristics, without impacting the performance of the cross‐traffic applications. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Modeling and performance analysis of VI‐CRA: A congestion control algorithm for vehicular networks

This study proposes a Vehicle ID‐based CAM Rate Adaptation (VI‐CRA) algorithm for beacon messages in the vehicular network. Foremost, an improved vehicle ID–based analytical model is proposed at the MAC layer of vehicular network. The model weighs the random back‐off number chosen by vehicles participating in the back‐off process, with the vehicle ID incorporated in their respective CAMs. This eventually leads to the selection of a vehicle ID–based random back‐off number, minimizing the probability of collision due to same back‐off number selection. It is worth noting that the improved analytical model outperforms the existing works in terms of average packet delay since only one fourth of the contention window size is used throughout the simulation. To enhance the performance of the analytical model, the paper incorporates a congestion control algorithm, by adapting the rate of CAM broadcast over the control channel. The algorithm is designed considering a wide range of scenarios, ranging from nonsaturated to extremely saturated network (in terms of collision probability) and sparsely distributed to teemed network (in terms of vehicular density). For better analyses of simulation results, the algorithm is applied over different vehicle ID–based back‐off numbers. Simulation results for all the back‐off numbers show that vehicle ID–based CAM rate adaptation algorithm performs better than the traditional fixed CAM rate IEEE 802.11p, even at high vehicular density.     

LTE‐DSRC hybrid vehicular communication system using DFT‐spread OFDM in uplink

Technologies are advancing at a rapid rate in the current era. People are advancing with the advancement of technologies, be it in education industry, health industry, and providing luxury. With the advent of autonomous luxury‐in‐motion car that provides in‐car‐entertainment (ICE), vehicular communication is the prerequisite towards achieving this goal. Dedicated short range communication (DSRC) is a matured vehicular communication standard, and Long‐Term Evolution (LTE) is the most competing technologies in the cellular communication. In this paper, we focus on the uplink performance of the LTE‐DSRC hybrid infrastructure. The basic transmission scheme for uplink direction is based on single carrier transmission in the form of discrete Fourier transform (DFT)‐spread Orthogonal Frequency Division Multiplexing (OFDM) with a minimum mean square error (MMSE) receiver at the LTE Evolved Node B (eNodeB) and a DSRC OFDM transmitter. A comprehensive bit error rate (BER) performance simulative study has been made on a color image transmission in uplink hybrid LTE‐DSRC system, and the results obtained are encouraging that the proposed convergence is possible, as it provides a substantial decrease in the BER with a gradual increase in the signal ‐to‐ noise ratio Signal to Noise Ratio (SNR).     

VDNet: an infrastructure‐less UAV‐assisted sparse VANET system with vehicle location prediction

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure‐less unmanned aerial vehicle (UAV) assisted VANET system called V ehicle‐D rone hybrid vehicular ad hoc Net work (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle‐to‐vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on‐board UAV, which can deliver data message directly to destination, relay messages in a multi‐hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end‐to‐end delay with controlled communication overhead. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive video protection in large scale peer‐to‐peer video streaming over mobile wireless mesh networks

Because video streaming over mobile handheld devices has been of great interest, the necessity of introducing new methods with low implementation cost and scalable infrastructures is a strong demand of the service. In particular, these requirements are present in popular wireless networks such as wireless mesh networks (WMN). Peer‐to‐peer (P2P) networks promise an efficient scalable network infrastructure for video streaming over wired and wireless networks. Limited resources of the peers in P2P networks and high error rate in wireless channels make it more challenging to run P2P streaming applications over WMNs. Therefore, it is necessary to design efficient and improved error protection methods in P2P video streaming applications over WMNs. In this paper, we propose a new adaptive unequal video protection method specially intended for large scale P2P video streaming over mobile WMNs. Using this method, different frames have different priorities in receivers along the recovery process. Moreover, we precisely and completely evaluate different aspects related to frame protection in these networks using five important performance metrics including video distortion, late arrival distortion, end‐to‐end delay, overhead and initial start‐up delay. The results obtained from a precise simulation in OMNeT++ show that the proposed adaptive method significantly outperforms other solutions by providing better video quality on mobile wireless nodes. Copyright © 2015 John Wiley & Sons, Ltd.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

Segment‐aware dynamic routing for DASH flows over software‐defined networks

Most of the video streaming applications running over the Internet send video data over HTTP and provide an architecture for video clients to adapt video quality during streaming. In HTTP adaptive streaming, a raw video is encoded at various qualities, each encoded video file is divided into small segments, and the clients may change the segment quality by sending requests for segments having different qualities over time. MPEG has standardized dynamic adaptive streaming over HTTP (MPEG‐DASH) due to this tendency. In this work, we focus on DASH over software‐defined networks (SDN), and we dynamically reroute DASH flows by considering the current network capacity, available bandwidth of the paths, and bitrate of the segments in order to provide high quality of experience (QoE) and fairness among DASH clients. Simulations performed under various network conditions show that the proposed study provides higher QoE and fairness compared with the max‐flow routing approach.     

Performance modeling of MPEG‐4 video streaming over IEEE 802.11 using distribution coordination function

Delivering video streaming over wireless Internet is becoming increasingly popular. However, most of the research studies focused on the modeling analysis of system performance such as saturation throughput and channel utilization. Perceived quality of video streaming cannot be assessed solely based on the results of analytical models. In this paper, we propose a model to assess the perceived quality of MPEG‐4 video streaming over IEEE 802.11 distribution coordination function (DCF)‐based wireless local area networks. The analysis of our proposed model considers not only effects of losses such as collision loss from channel access competition but also wireless loss caused by wireless interferences. Moreover, the impact of the loss of specific MPEG‐4 video frames is also taken into account in the performance analysis. The model was validated by comparing our performance results with results obtained from simulation and analytical models. The results show that our proposed model is able to predict the perceived quality of MPEG‐4 video streaming over DCF‐based WLAN more accurately than other models. Copyright © 2010 John Wiley & Sons, Ltd.     

An IPv6‐based mobility framework for urban vehicular networks

This paper proposes an IPv6‐based mobility framework for urban vehicular networks. In this framework, the architecture for urban vehicular networks is presented, and based on this architecture, the IPv6 address structure for urban vehicular networks is proposed. In this framework, a vehicle is always identified by its home address and does not need to be configured with a care‐of address. Based on this architecture, the pre‐handover algorithm is presented. In this algorithm, the third‐layer (L3) mobility handover is performed before the second‐layer (L2) one. When a vehicle finishes the L3 handover but does not perform the L2 handover, the corresponding access router or border router reserves the messages destined for the vehicle. After the vehicle completes the L2 handover, the access router or border router transmits the reserved messages to the vehicle. The performance of this framework is evaluated, and the results show that this framework reduces the handover cost and delay and lowers the packet loss. Copyright © 2015 John Wiley & Sons, Ltd.     

Muslin: A QoE‐aware CDN resources provisioning and advertising system for cost‐efficient multisource live streaming

Delivering video content with a high and fairly shared quality of experience is a challenging task in view of the drastic video traffic increase forecasts, as live video traffic will grow 15‐fold by 2022. Currently, content delivery networks provide numerous servers hosting replicas of the video content, and consuming clients are redirected to the closest server. Then, the video content is streamed using adaptive streaming solutions. However, servers and network links often become overloaded during major events, and users may experience a poor or unfairly distributed quality of experience, unless more servers are provisioned. In this paper, we propose Muslin , a streaming solution supporting a high, fairly shared end users' quality of experience for live streaming, while minimizing the required content delivery platform scale. Muslin leverages on MS‐Stream, a content delivery solution, which aggregates video content from multiple servers to offer a high quality of experience for its users. Muslin dynamically provisions servers and replicates content into servers and advertises servers to clients based on real‐time delivery conditions. We have used Muslin to replay a 1‐day video‐games event, with hundreds of clients and several test beds. Our results show that our approach outperforms traditional content delivery schemes by increasing the fairness and quality of experience at the user side with a smaller infrastructure scale.     

Visual‐Attention‐Aware Progressive RoI Trick Mode Streaming in Interactive Panoramic Video Service

In the near future, traditional narrow and fixed viewpoint video services will be replaced by high‐quality panorama video services. This paper proposes a visual‐attention‐aware progressive region of interest (RoI) trick mode streaming service (VA‐PRTS) that prioritizes video data to transmit according to the visual attention and transmits prioritized video data progressively. VA‐PRTS enables the receiver to speed up the time to display without degrading the perceptual quality. For the proposed VA‐PRTS, this paper defines a cutoff visual attention metric algorithm to determine the quality of the encoded video slice based on the capability of visual attention and the progressive streaming method based on the priority of RoI video data. Compared to conventional methods, VA‐PRTS increases the bitrate saving by over 57% and decreases the interactive delay by over 66%, while maintaining a level of perceptual video quality. The experiment results show that the proposed VA‐PRTS improves the quality of the viewer experience for interactive panoramic video streaming services. The development results show that the VA‐PRTS has highly practical real‐field feasibility.