Title
Crowdsourced Multi-View Live Video Streaming using Cloud Computing.
Abstract
Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowdsourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch).
Year
DOI
Venue
2017
10.1109/ACCESS.2017.2720189
IEEE ACCESS
Keywords
Field
DocType
Cloud,crowdsourcing,multi-view video,QoE,resource allocation
Resource management,Metadata,Transcoding,Computer science,Heuristic (computer science),Resource allocation,Bandwidth (signal processing),Integer programming,Multimedia,Cloud computing
Journal
Volume
ISSN
Citations 
5
2169-3536
3
PageRank 
References 
Authors
0.38
23
3
Name
Order
Citations
PageRank
Kashif Bilal144325.77
Aiman Erbad225131.97
Mohamed Hefeeda31569111.28