Optimizing Cloud Base Video – CrowdSensing (original) (raw)
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IRJET-On Demand Retrieval of Crowd Sourced Mobile Video streaming and sharing the video: CCMVA
while demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed CCMVA, which has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the CCMVA-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.
Crowdsourced Multi-View Live Video Streaming using Cloud Computing
IEEE Access
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). INDEX TERMS Cloud, crowdsourcing, multi-view video, QoE, resource allocation.
A Cloud-Based Architecture for Video Services in Crowd Events
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In this paper, we discuss a use case focused on crowd events in the context of the original 5G ESSENCE project [1], emphasizing on the provision of video services to the involved end-users. In particular, we investigate the core architectural components, as well as the cloud testbed for the deployment of our use case.
IJERT-Empirical Analysis of User Based Cloud Mobile Video Streaming
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/empirical-analysis-of-user-based-cloud-mobile-video-streaming https://www.ijert.org/research/empirical-analysis-of-user-based-cloud-mobile-video-streaming-IJERTV3IS20164.pdf A mobile network has unpleasant because of the demands on video traffic; the wireless link capacity cannot keep up with the traffic claim. The break between the traffic claim and the link capability, along with time-varying link conditions, produce the results in poor service quality of video streaming such as long buffering time and irregular disruptions. The cloud computing technology, propose a new mobile video streaming framework of cloud, which has two main parts: adaptive mobile video streaming and efficient social video sharing. It constructs a private agent to provide video streaming services efficiently for each mobile user. Adaptive mobile video streaming lets the private agent adaptively adjust the streaming with a scalable video coding technique based on the feedback of link quality. Efficient social video sharing monitors the social network interactions among mobile users and their private agents try to pref-etch video content in advance. Scalable video coding and adaptive streaming techniques can be jointly combined to accomplish effectively the best possible quality of video streaming services. To implement a prototype of the Cloud framework to demonstrate its concert. It is shown that the private agent in the clouds can effectively provide the adaptive streaming, and carry out video sharing (i.e., pref-etching) based on the social network analysis. Carry out large-scale implementation and with serious consideration on energy and price cost and ignored the cost of encoding workload in the cloud while implementing the prototype.
In the recent times there has been numerous learning to recover the service excellence of video streaming of mobile on features such as scalability and Adaptability. Scalable video coding in addition to techniques of adaptive stream are mutually united towards achieving efficiently finest probable superiority of services of stream of video. In wireless systems, due to mobility of users, mobile nodes generously, transform their points of attachment towards network, which is function consequently referred as handoff. to make sure a well-organized provision of instantaneous video applications in systems of wireless, mobile users have to be capable to energetically negotiate their QoS needs, represented by service level provisions, with access network. The propagation of mobile applications through video streaming potential means that traffic of mobile video is quickly becoming foremost form in mobile networks. Applying information visualization towards programming, research in visual l...
Survey on Crowd-Source Video Sharing Systems
Nowadays video capturing from mobile and sharing it is common. Consider it be any event, function, performance by an artist, any surprising event. For example, if any famous speaker addressing huge number of people, then this event there may be many people who capture the event in their mobile phones and uploaded it on the video sharing applications (VSA) such as YouTube, Twitter, Facebook. Same event is captured by the hundreds of the devices and uploaded on VSA. This leads to some issues like, huge amount of the bandwidth and battery is use by this activity of uploading videos of the same event and it might also cause the problem while retrieving that video. Numbers of approaches are proposed in the literature to address such problems such as on-demand approach in which first user query is matched to the metadata stored at the server and then data is fetched from user who uploaded the video.We described number of techniques available which gives solution for video sharing and retrieval problems.
Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions
IEEE Communications Surveys & Tutorials, 2021
Future wireless networks will provide highbandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined. Index Terms-Wireless communications, 5G networks, Internet of Things, mobile edge computing, edge analytics, video analytics, caching, task offloading, video streaming, quality of experience. I. INTRODUCTION T HE GLOBAL mobile traffic is expected to grow about eight times by the year 2022, where video data will account for about 80% of the traffic [1]. This is not surprising, given that about 60% of the worldwide population has watched videos on their mobile devices in 2018 [2]. In general, videos Manuscript