MERVS: A Novel Multichannel Error Recovery Video Streaming Protocol for Vehicle Ad Hoc Networks

Xie, Hengheng; Boukerche, Azzedine; Loureiro, Antonio

doi:10.1109/tvt.2015.2397862

Cited by 32 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cost, F onserver , for which a task occupies the resource on MEC server can be estimated based on the serving time as follows. F onserver = T onserver × Price onserver (5) where Price onserver is the serving cost of MEC server for every time unit and T onserver is the time for which the task occupies resources of the MEC server. Moreover, T onserver can be expressed as the summation of t queue and t computing that are the time for the task in queue and in computation, respectively.…”

Section: Queue Model Of Mec Servermentioning

confidence: 99%

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Zhou

Xiong³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

As autonomous and connected vehicles are becoming a reality, mobile-edge computing (MEC) off-loading provides a promising paradigm to trade off between the long latency of clouding computing and the high cost of upgrading the on-board computers of vehicles. However, due to the randomness of task arrivals, vehicles always have a tendency to choose MEC server for offloading in a selfish way, which is not satisfactory for the social good of the whole system and even results in a failure possibility of some tasks due to the overflow of MEC servers. This paper elaborates the modeling of task arrival process and the influence of various offloading modes on computation cost. Interestingly, by formulating task arrivals as a compound process of vehicle arrivals and task generations, we found that the task arrival model for MEC servers does not belong to the standard Poisson distribution, which contradicts the popular assumption in most existing studies. Considering the load distribution and the prediction of cost, we propose a load-aware MEC offloading method, in which each vehicle makes MEC server selection based on the predicted cost with the updated knowledge on load distribution of MEC servers. Analysis and simulation show that the proposed scheme can achieve up to 65% reduction of total cost with almost 100% task success ratio. INDEX TERMS Mobile-edge computing, task arrival model, load-aware offloading, vehicular networks, load balance. I. INTRODUCTION With the development of the Internet of Vehicles (IoV), autonomous and connected vehicles are envisioned to provide a safer, greener [1], [2] and much more convenient [3], [4] transportation system for the public. Internet of vehicles are based on a core technology called Vehicular Ad Hoc Networks (VANETs) [5], [6], which is able to integrate the capabilities of wireless networks to vehicles. Vehicles may communicate with other vehicles directly in a vehicle-tovehicle fashion (V2V) [7], or communicate through road side units(RSUs), so called Vehicle-to-Infrastructure (V2I) communications [8]. IEEE 802 committee has defined wireless communication standard IEEE 802.11p [9] for V2I The associate editor coordinating the review of this manuscript and approving it for publication was Xu Chen. communication and the Federal Communications Commission allocated 75 MHz of bandwidth, which operates on 5.9 GHz channel for short range communications [10]. Due to the resource-limited on-board computers of vehicles, many potential applications, such as assistant accident avoidance [11]-[13], mobile crowd sensing [14] and augmented reality [15], which require significant computing power to process data generated by the vehicle sensors in a real-time fashion [16], pose a grand challenge to vehicular terminals. To offload the computation burden of vehicles, cloud computing [17], [18] has been adopted by gathering the computation tasks to central cloud servers, and sending back the results to local users. However, the long distance between cloud center and vehicles [19] results in long l...

show abstract

Section: Queue Model Of Mec Servermentioning

confidence: 99%

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Zhou

Xiong³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Although, the SSIM index is among the most reliable metrics for video quality evaluation but not considered in this study. A novel Multichannel Error Recovery Video Streaming (MERVS) protocol is proposed to address issue of high packet loss in VANETs [95]. MERVS uses an error recovery process for high quality and real time video streaming.…”

Section: Video Streaming Over Vehicular Ad-hoc Networkmentioning

confidence: 99%

“…The Peak Signal-to-Noise Ratio (PSNR) is an expression for the ratio between maximum probable value of a signal and the power of distorting noise that affects the quality of its representation. Because, many signals have a very wide dynamic range, (ratio between the largest and smallest probable values of a variable quantity) [20,95,129]. The PSNR is generally expressed in terms of the logarithmic decibel scale, see Eq.…”

Section:  Peak Signal-to-noise Ratio (Psnr)mentioning

confidence: 99%

See 1 more Smart Citation

Towards video streaming in IoT Environments: Vehicular communication perspective

Aliyu

Abdullah

Kaiwartya

et al. 2018

Computer Communications

View full text Add to dashboard Cite

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-tovehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, the significance of video streaming in vehicular IoT environments is highlighted focusing on the integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues.

show abstract

“…Their work lacks in accuracy because they use the 802.11b/g/n protocols instead of the 802.11p protocol, which is the protocol specifically developed for vehicular networks. In [ 11 ], the authors present the Multichannel Error Recovery Video Streaming (MERVS) scheme, a streaming protocol in which the most relevant video frames are transmitted using the Transmission Control Protocol (TCP) protocol, and the least relevant video frames are transmitted using the User Datagram Protocol (UDP) protocol. TCP is in charge of retransmitting lost frames, so the protection is guaranteed by this protocol.…”

Section: Related Workmentioning

confidence: 99%

Error Resilient Coding Techniques for Video Delivery over Vehicular Networks

Piñol

Martínez-Rach

Garrido

et al. 2018

Sensors

View full text Add to dashboard Cite

Nowadays, more and more vehicles are equipped with communication capabilities, not only providing connectivity with onboard devices, but also with off-board communication infrastructures. From road safety (i.e., multimedia e-call) to infotainment (i.e., video on demand services), there are a lot of applications and services that may be deployed in vehicular networks, where video streaming is the key factor. As it is well known, these networks suffer from high interference levels and low available network resources, and it is a great challenge to deploy video delivery applications which provide good quality video services. We focus our work on supplying error resilience capabilities to video streams in order to fight against the high packet loss rates found in vehicular networks. So, we propose the combination of source coding and channel coding techniques. The former ones are applied in the video encoding process by means of intra-refresh coding modes and tile-based frame partitioning techniques. The latter one is based on the use of forward error correction mechanisms in order to recover as many lost packets as possible. We have carried out an extensive evaluation process to measure the error resilience capabilities of both approaches in both (a) a simple packet error probabilistic model, and (b) a realistic vehicular network simulation framework. Results show that forward error correction mechanisms are mandatory to guarantee video delivery with an acceptable quality level , and we highly recommend the use of the proposed mechanisms to increase even more the final video quality.

show abstract

MERVS: A Novel Multichannel Error Recovery Video Streaming Protocol for Vehicle Ad Hoc Networks

Cited by 32 publications

References 29 publications

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Compound Model of Task Arrivals and Load-Aware Offloading for Vehicular Mobile Edge Computing Networks

Towards video streaming in IoT Environments: Vehicular communication perspective

Error Resilient Coding Techniques for Video Delivery over Vehicular Networks

Contact Info

Product

Resources

About