VSync: Cloud based video streaming service for mobile devices

Baik, Eilwoo; Pande, Amit; Zheng, Zizhan; Mohapatra, Prasant

doi:10.1109/infocom.2016.7524567

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…The Vsync method was introduced in REf. [21] as a technique for achieving synchronisation of mobile video content through cloud computing. The use of a cloud-based framework for real-time transcoding and transmission guarantees a superior level of video quality during video streaming.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of an Adaptive Multimedia Streaming in Mobile Cloud Computing for Slow-Speed Networks

Ojo,

Oyinloye,

Adejimi

et al. 2023

Afr. Sci. Rep.

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Multimedia cloud (MC) is an aspect of cloud computing that facilitates the effective use of multimedia services by end users in the context of cloud infrastructures. Despite rising network traffic, cloud computing technology provides novel strategies for disseminating visual content; adaptive encoding is implemented at the cloud server to optimise performance. However, streaming video over the Internet has caused a slew of problems, including sporadic interruptions, delays, inadequate bandwidth, and oscillating link conditions, all of which contribute to poor quality of service (QoS). This study presents an adaptable streaming method to grapple with delays, sporadic interruptions, and bandwidth alterations. The adaption logic concept was used to develop the scheme, which was then put into practice utilising the Java programming language and cloud computing. A variety of network circumstances were used to test the method using pre-recorded video sequences that were separated into chunks with fewer frames. The evaluation findings showed that the suggested streaming technique can dynamically adapt to different bandwidth changes, making it ideal for slow-speed network situations. The system is also capable of delivering seamless, interruption-free video playback.

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Section: Related Workmentioning

confidence: 99%

Evaluation of an Adaptive Multimedia Streaming in Mobile Cloud Computing for Slow-Speed Networks

Ojo,

Oyinloye,

Adejimi

et al. 2023

Afr. Sci. Rep.

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show abstract

“…Nearly every corporate public cloud service, such as Spritdsp, Amazon Web Services (AWS) and Bitcodin helps manage the transcoding service of a user to create a different solution for uploading video files. In multiple devices, a user can experience online video content with varying sizes of resolution [40].…”

Section: Transcodingmentioning

confidence: 99%

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Tamizhselvi

Muthuswamy

2021

Peer-to-Peer Netw. Appl.

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In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server’s utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

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“…To minimize the video streaming delay for such diverse viewers, Streaming Service Providers (SSPs) commonly pretranscode videos, i.e., they store several versions of the same video [5], [6]. Given the volume of videos that needs to be transcoded and stored, this approach requires massive storage and processing resources.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

Salehi

Joshi

et al. 2019

IEEE Trans. Parallel Distrib. Syst.

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High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (i.e., transcoding) video streams to match the characteristics of viewers' devices (e.g., in terms of spatial resolution or supported formats). Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers. Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices. Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs. In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs. Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding. Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks. The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.

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VSync: Cloud based video streaming service for mobile devices

Cited by 13 publications

References 24 publications

Evaluation of an Adaptive Multimedia Streaming in Mobile Cloud Computing for Slow-Speed Networks

Evaluation of an Adaptive Multimedia Streaming in Mobile Cloud Computing for Slow-Speed Networks

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Performance Analysis and Modeling of Video Transcoding Using Heterogeneous Cloud Services

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