ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences Using Artificial Neural Network

Zabrovskiy, Anatoliy; Agrawal, Prateek; Mathá, Roland; Timmerer, Christian; Prodan, Radu

doi:10.1109/bigmm50055.2020.00056

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…For the actual implementation, we transcode the full set of representations (i.e., we adopt the bitrate configuration of HEVC/H.265 30 fps from [35]) of the BasketballDrive [13] sequence using HEVC HM-16.20 [13] with four-second segment length. It is worth mentioning that transcoding time depends on the video content complexity [36]. However, as the encoding process at the edge (as a part of a transcoding operation) is limited to the optimal decisions stored as metadata, there was not a noticeable difference between "easy to encode" and "hard to encode" videos for both metadata size and transcoding time in our experiments.…”

Section: A Evaluation Setup and Overviewmentioning

confidence: 85%

LwTE: Light-Weight Transcoding at the Edge

et al. 2021

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Due to the growing demand for video streaming services, providers have to deal with increasing resource requirements for increasingly heterogeneous environments. To mitigate this problem, many works have been proposed which aim to (i) improve cloud/edge caching efficiency, (ii) use computation power available in the cloud/edge for on-the-fly transcoding, and (iii) optimize the trade-off among various cost parameters, e.g., storage, computation, and bandwidth. In this paper, we propose LwTE, a novel Light-weight Transcoding approach at the Edge, in the context of HTTP Adaptive Streaming (HAS). During the encoding process of a video segment at the origin side, computationally intense search processes are going on. The main idea of LwTE is to store the optimal results of these search processes as metadata for each video bitrate and reuse them at the edge servers to reduce the required time and computational resources for on-thefly transcoding. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. In this way, in addition to the significant reduction in bandwidth and storage consumption, the required time for on-the-fly transcoding of a requested segment is remarkably decreased by utilizing its corresponding metadata; unnecessary search processes are avoided. Popular video segments/bitrates are being stored. We investigate our approach for Video-on-Demand (VoD) streaming services by optimizing storage and computation (transcoding) costs at the edge servers and then compare it to conventional methods (store all bitrates, partial transcoding). The results indicate that our approach reduces the transcoding time by at least 80% and decreases the aforementioned costs by 12% to 70% compared to the state-of-the-art approaches.INDEX TERMS Video streaming, transcoding, video on demand, edge computing.

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Section: A Evaluation Setup and Overviewmentioning

confidence: 85%

LwTE: Light-Weight Transcoding at the Edge

et al. 2021

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“…We encoded each Y4M segment using the FFmpeg ×264 video codec implementation with the veryslow encoding preset to get the highest possible quality compared to the original videos. The ×264 video codec contains nine encodings presets: ultrafast, superfast, veryfast, faster, fast, medium (default preset), slow, slower, veryslow, placebo [44]. Encoding bitrate with a slower ×264 encoding preset for the same video usually has a slower encoding speed but better visual quality [45].…”

Section: Ec2 Instance Performance Analysismentioning

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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“…Naïve Bayes This classifier superintends ML algorithms utilizing Bayes theorem and acts on foundation whose features are analytically independent. This theorem relies on naïve assumption, where input factors are independent of each other [48][49][50][51]. Naive bayes formula is given below:…”

Section: Gaussian Naive Bayesmentioning

confidence: 99%

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Malik¹,

Nandal²,

Dalal³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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It has been observed that driver behavior has a direct and considerable impact upon factors like fuel consumption, environmentally harmful emissions, and public safety, making it a key consideration of further research in order to monitor and control such related hazards. This has fueled our decision to conduct a study in order to arrive at an efficient way of analyzing the various parameters of driver behavior and find ways and means of positively impacting such behavior. It has been ascertained that such behavioral patterns can significantly impact the analysis of traffic-related conditions and outcomes. In such cases, the specific vehicular behavior can be detected and related data mined in order to analyze the spatial or temporal patterns of movement patterns as well as to position/track the prominent trends. This analysis seeks to determine the efficacy of such an exercise and whether the various parameters employed can help efficiently determine the various criteria for defining the driver's style. To that end, the analysis of a driver's behavioral pattern and performance utilizes a computer modeled application for generating a set of classifications based on the autonomous driving data as well as indicators that are characteristic of driver aggression. In order to draw such insights from the driver's behavior, the application is modeled using various categories of data, for instance, the steering wheel's angle, braking conditions, acceleration conditions, the vehicle speed, etc. Unlike the previously developed mechanisms for analyzing the system-based driver behavioral patterns, which were not very efficacious, this endeavor assimilates the contemporary breakthroughs in real-world scenario analysis approaches and driver behavior classification methods. Based on the system capabilities and desired outcomes, distinct strategies can be employed in order to detect the target driver's behavior. In this specific case, neural network algorithms were utilized in order to conduct an intensive study to determine and analyze the prevailing driver behavior and driving styles. This proposed approach evaluated multiple factors that were determinants in identifying specific driver behavior and driving styles. The results of this experiment that utilized Python, indicated that the driver model in question was successful in achieving a 90% accuracy in terms of logistic regression.

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ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences Using Artificial Neural Network

Cited by 9 publications

References 29 publications

LwTE: Light-Weight Transcoding at the Edge

LwTE: Light-Weight Transcoding at the Edge

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

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

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