QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei, Lei; Cai, Jianfei; Foh, Chuan Heng; He, Bingsheng

doi:10.1109/tcsvt.2016.2589621

Cited by 64 publications

(35 citation statements)

References 31 publications

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“…The performance evaluation results show that the proposed framework provides instant playback for live services with low start-up delay and avoids video stalling under dynamic network conditions. A cloud-based online video transcoding (COVT) system was also proposed by Wei et al [76]. COVT aims to minimize the amount of CPU resources given specific QoS constrains, such as system delay and targeted chunk size.…”

Section: Transcoding-based Solutionsmentioning

confidence: 99%

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

Trestian

Comşa

Tüysüz

2018

IEEE Commun. Surv. Tutorials

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Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge.Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s).Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-theart approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided.

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Section: Transcoding-based Solutionsmentioning

confidence: 99%

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

Trestian

Comşa

Tüysüz

2018

IEEE Commun. Surv. Tutorials

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“…This approach, however, requires a massive amount of computing resources for video transcoding and produces extreme processing delays [17].…”

Section: ) Online Video Transcodingmentioning

confidence: 99%

A Private Cloud-Based Video Transcoding Architecture for Project-Based Learning Environment

Ibrahim¹,

Adnan

Zainol

et al. 2018

IJMLC

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Abstract-Project-based learning (PBL) is a teaching method that integrates real-world problems and technology into the curriculum. The use of mobile and video technologies in PBL has made the learning environment much more interactive. However, the gap between the richness of video content and the limited capabilities of mobile devices still remains a challenge. In order to solve this problem, video transcoding can be implemented. However, this solution may provide extreme CPU consumption and processing delay on the server's side. To overcome this issue, a private cloud-based video transcoding architecture is proposed. In this paper, the proposed solution was designed, implemented and compared with the server-based content switching method, and an experiment was conducted to evaluate the method in large-scale simulated workloads. The results show that the proposed architecture is capable of dealing with many concurrent users' access and capable of reducing the video downloading time.Index Terms-Cloud computing, project-based learning, video transcoding.

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“…In the context of cloud-based video transcoding, some research efforts investigate the advantages of cloud computing and devise joint processing resource allocation and scheduling policies to reduce the transcoding delays in the delivery phase [9], [31]. In addition, [5]- [7] investigate joint multi-bitrate video caching and transcoding by utilizing the ABR streaming technology in C-RANs.…”

Section: Introductionmentioning

confidence: 99%

“…However, non-simultaneous transferring and transcoding video files wastes more time and physical resources in the CVCT system, which is not beneficial for delay-sensitive services. To cope with this challenge, parallel video transmission and transcoding capability [9], [14] can be deployed.In the parallel CVCT system, video transcoding runs in parallel with video transmission, and all the multi-hop video transmissions (between backhaul, fronthaul, and wireless access links) also run in parallel.Non-orthogonal multiple access (NOMA) has recently considered as a promising technology to improve the spectral efficiency of 5G wireless networks [15], [16]. Unlike conventional orthogonal multiple access (OMA) techniques, NOMA can significantly improve the system throughput and support the massive connectivity by using successful interference cancellation (SIC) at the receivers and a mixture of multiple messages at the transmitter [15], [16].…”

mentioning

confidence: 99%

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

et al. 2019

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Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and 5G beyond, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier nonorthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on the user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational Sepehr Rezvani and Nader Mokari are with the Recently, mobile edge computing (MEC) networks have emerged as a promising technology for next generation wireless networks, providing cloud caching and computing capabilities within the RAN [8], [10]-[13]. Thanks to this paradigm, video files could be prefetched and/or transcoded in close proximity to end-users, leading to enormous latency and backhaul traffic reductions in wireless networks. One problem with this, however, is that duplicated video caching and transcoding in multiple resource-constrained MEC servers wastes both storage and processing resources. To tackle this issue, cooperative joint multi-bitrate video caching and transcoding (CVCT) technology is proposed where each MEC server is able to receive the requested video files from neighboring MEC servers via fronthaul links [7]. In this architecture, each MEC server is deployed side-by-side with each base station (BS) using the generic computing platforms which provides the caching and computation capabilities in heterogeneous networks (HetNets) [5]-[8].3 By sharing both the storage and processing resources among multiple MEC servers, more video files can be prefetched within RANs which results increasing the cache hit ratio [7], [8]. However, non-simultaneous transferring and transcoding video files wastes more time and physical resources in the CVCT system, which is not beneficial for delay-sensitive services. To cope with this challenge, parallel video transmission and transcoding capability [9], [14] can be deployed.In the parallel CVCT system, video transcoding runs in parallel with video transmission, and all the multi-hop video transmissions (between backhaul, fronthaul, and wireless access links) also run in parall...

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QoS-Aware Resource Allocation for Video Transcoding in Clouds

Cited by 64 publications

References 31 publications

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

A Private Cloud-Based Video Transcoding Architecture for Project-Based Learning Environment

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

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