Cloud-Assisted Live Streaming for Crowdsourced Multimedia Content

Chen, Fei; Zhang, Cong; Wang, Feng; Liu, Jiangchuan; Wang, Xiaofeng; Liu, Yuan

doi:10.1109/tmm.2015.2460193

Cited by 30 publications

(8 citation statements)

References 22 publications

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“…As multimedia requires more bandwidth, processing, and storage, so handling such huge volumes in terms of communication, processing, and storage is a real challenge. Edge computing is envisioned to aid in such scenarios to minimize the overall end-to-end bandwidth usage, distribution, efficient processing, and storage for multimedia [79][80] [81]. Multimedia delivery also incurs high costs.…”

Section: Multimedia and Edge Computingmentioning

confidence: 99%

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

et al. 2018

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Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name "edge computing" for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of "being near" to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users' request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service provider's data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.

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Section: Multimedia and Edge Computingmentioning

confidence: 99%

Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers

et al. 2018

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“…Fei Chen et al [4] reports the advantages of crowdsourcing in media generation and distribution. Conventional streaming methods includes large number of data channels to minimize the latency.…”

Section: Related Workmentioning

confidence: 99%

Ranked k-NN Crowdsourced Model for Cloud Internet of Things (CIoT)

Duraipandian¹

2020

JISMAC

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Internet of Things (IoT) has gained more attention in recent years and its influence over future internet is projected to be more as a promising technology. IoT enables sensors to merge with smart devices to monitor, observe and analyse the real time data. These features make the IoT, a suitable technology, for smart applications. On the other hand, cloud offers a better computing paradigm to store and analyse the data. Cloud reduces the complexities in day today life with its novel applications and services, in an efficient manner. However, present IoT and Cloud solutions are focused towards centralized solutions, which limits the user capacity. To enrich the Cloud integrated IoT benefits, a flexible large-scale data collection and analysis is introduced as crowdsourcing, which provides a new dimension in data mining applications. This research work presents a cloud computing crowdsourced data analysis model implemented over IoT, to obtain better computation speed with improved sensitivity, specificity and accuracy.

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“…In the past few years, videos streamed on CLVS platforms have generated a staggering amount of traffic: 3.8 million people broadcasted livestreaming videos [3] in Feb. 2020 on Twitch [4] , a CLVS platform. This amount of traffic [5] requires a high capacity network with low latency network paths for multiple video streams. Furthermore, a CLVS-based NS may also require compute resources to process the video traffic [6] .…”

Section: Crowdsourced Live Video Streaming Network Servicementioning

confidence: 99%