Energy-Efficient Proactive Caching for Adaptive Video Streaming via Data-Driven Optimization

Li, Liang; Shi, Dian; Hou, Ronghui; Chen, Rui; Lin, Bin; Pan, Miao

doi:10.1109/jiot.2020.2981250

Cited by 48 publications

(26 citation statements)

References 35 publications

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“…Simultaneously, in this contribution, the optimization problems were further considered in (i) joint optimization of access control and resource allocation and (ii) joint optimization of caching decision and transcoding strategies. In [107], the total expected energy consumption in an LVS service was minimized via the MEC support along with caching, transcoding, backhaul retrieving, and ABS platforms. The results obtained from [107] show not only the optimal energy scheme but also the efectiveness of the cache hit ratio.…”

Section: Resource Consumptionmentioning

confidence: 99%

“…In [107], the total expected energy consumption in an LVS service was minimized via the MEC support along with caching, transcoding, backhaul retrieving, and ABS platforms. The results obtained from [107] show not only the optimal energy scheme but also the efectiveness of the cache hit ratio. In [178], an online learning algorithm without training phases was proposed to actively estimate user preferences according to user feedback based on regression analysis, from which the optimal edge resource allocation strategy regarding computing, caching, and bandwidth parameters for MEC-based LVS services was developed.…”

Section: Resource Consumptionmentioning

confidence: 99%

“…In [178], an online learning algorithm without training phases was proposed to actively estimate user preferences according to user feedback based on regression analysis, from which the optimal edge resource allocation strategy regarding computing, caching, and bandwidth parameters for MEC-based LVS services was developed. Unlike [23,66,106,107,110,178], without cloud/edge platforms, Erfanian et al [43] have recently introduced an optimizing available resource utilization strategy that focuses on the bandwidth resource for LVS based on SDN, NFV, and mCast support, where the requirement of the E2E latency threshold is satisied.…”

Section: Resource Consumptionmentioning

confidence: 99%

See 2 more Smart Citations

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Dao

Tran

et al. 2022

ACM Comput. Surv.

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Live video streaming services have experienced significant growth since the emergence of social networking paradigms in recent years. In this scenario, adaptive bitrate streaming communications transmitted on web protocols provide a convenient and cost-efficient facility to serve various multimedia platforms over the Internet. In these communication models, video content is delivered optimally, possibly transcoded, edited automatically, and cached temporarily by network elements along the path. To this end, the computational capabilities of various network elements are considered as major resources to be optimized for service quality improvements. This paper provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. First, an overview of the global standards, system architectures, and streaming protocols is presented. Next, hierarchical computation-driven models of live video streaming are anatomized, including cloud-, edge-, and peer-to-peer-based solutions. Cutting-edge studies are then reviewed to discover the advances they have made in improving system performance in multiple aspects. Finally, open challenges are presented to direct future research in this field.

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Section: Resource Consumptionmentioning

confidence: 99%

Section: Resource Consumptionmentioning

confidence: 99%

Section: Resource Consumptionmentioning

confidence: 99%

See 1 more Smart Citation

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Dao

Tran

et al. 2022

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…The authors performed benchmarking to find the optimal parallelism for interactive streaming video. Li et al [39] proposed a HAS delivery scheme that combines caching, transcoding for energy and resource-efficient scheduling. Ma [40] proposed a scheduling method for transcoding MPEG-DASH video segments using a node that managed all other servers in the system (rather than predicting the transcoding times) and reported a saving time of up to 30%.…”

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

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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.

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“…The authors use benchmarking to find the optimal parallelism for interactive streaming video. Li et al [35] proposed a HAS delivery scheme that combines caching, transcoding for energy and resource efficient scheduling. Mostafa et al [36] presented a a moth-flame optimization algorithm that defines and assigns the appropriate jobs to fog computing units to reduce the total task execution time, evaluated using the iFogSim toolkit [37].…”

Section: Transcoding Task 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

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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.

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Energy-Efficient Proactive Caching for Adaptive Video Streaming via Data-Driven Optimization

Cited by 48 publications

References 35 publications

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

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

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