MCCA scheduling for enhancing QoS based video streaming for video surveillance applications

Kirubha, D.; Ramar, K.

doi:10.1007/s10586-018-2150-y

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some recent remarkable works contributed to scheduling the video transcoding tasks [32][33][34]. Kirubha et al [35] implemented a modified controlled channel access scheduling method to improve the quality of service-based video streaming. Similarly, Jokhio et al [36] presented a distributed video transcoding method to reduce video bitrates.…”

Section: Video Transcoding-specific Scheduling Techniquesmentioning

confidence: 99%

FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

Zabrovskiy¹,

Agrawal²,

Kashansky³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today's network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients' budgets. Thus, there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations. Additionally, the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content. In this paper, we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple ×264 codec encoding parameters and video sequences of varying complexity. Then, we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs. Furthermore, we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost. The results show that our approach, on average, can reduce the encoding costs by at least 15.8% and up to 47.8% when compared to a random selection of EC2 spot instances.

show abstract

Section: Video Transcoding-specific Scheduling Techniquesmentioning

confidence: 99%

FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

Zabrovskiy¹,

Agrawal²,

Kashansky³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Kirubha and Ramar [21] implemented a modified controlled channel access scheduling method to improve the quality of service-based video streaming. Munagala and Prasad [22] proposed a cluster entropy-based transcoding method for efficient video encoding and compression, experimented on eight video sequences.…”

Section: Cluster Computing Schedulingmentioning

confidence: 99%

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Agrawal

Zabrovskiy

Ilangovan³

et al. 2020

Cluster Comput

View full text Add to dashboard Cite

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

show abstract

Live Streaming Architectures for Video Data: A Review

Ginimav¹,

Prasad²,

Gowrishankar³

2020

JISMAC

View full text Add to dashboard Cite

Video communication has become a crucial part of our everyday lives due to its ease of use and applicability. Also, the demand for video streaming services has grown exponentially due to the recent Covid19 situation. Over the years, researchers have formulated different algorithms and architectures for effective video streaming. Due to the presence of a large variety of algorithms, system designers are often unable to decide the most optimum video streaming architectures for effective system design. In this work, some of the most recent and effective streaming architectures and algorithms are discussed along with their performance metrics. This will assist readers in deciding the most optimum algorithms and architectures for their application. Along with the algorithmic comparisons, this text also recommends certain improvements in the reviewed algorithms for further improving their performance.

show abstract

MCCA scheduling for enhancing QoS based video streaming for video surveillance applications

Cited by 3 publications

References 11 publications

FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

Live Streaming Architectures for Video Data: A Review

Contact Info

Product

Resources

About