A real-time multimedia streaming transmission control mechanism based on edge cloud computing and opportunistic approximation optimization

Jin, Yanliang; Qian, Zhenjiang; Sun, Gaofei

doi:10.1007/s11042-018-6680-3

Cited by 9 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction module was executed against the testing dataset with 168,631 frames, and the inference latency was measured for three GPU-based instances. The results shown in Table 1 demonstrate a consistent pattern of significantly lower latency for ViT_8_56 than the original model across all instances, which was aligned with the literature [70,71], where the optimized model predicted the testing set faster than the original model by a factor of 1.67.…”

Section: Discussionsupporting

confidence: 80%

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

Sarraf,

Kabia

2023

MAKE

View full text Add to dashboard Cite

This study introduces an optimal topology of vision transformers for real-time video action recognition in a cloud-based solution. Although model performance is a key criterion for real-time video analysis use cases, inference latency plays a more crucial role in adopting such technology in real-world scenarios. Our objective is to reduce the inference latency of the solution while admissibly maintaining the vision transformer’s performance. Thus, we employed the optimal cloud components as the foundation of our machine learning pipeline and optimized the topology of vision transformers. We utilized UCF101, including more than one million action recognition video clips. The modeling pipeline consists of a preprocessing module to extract frames from video clips, training two-dimensional (2D) vision transformer models, and deep learning baselines. The pipeline also includes a postprocessing step to aggregate the frame-level predictions to generate the video-level predictions at inference. The results demonstrate that our optimal vision transformer model with an input dimension of 56 × 56 × 3 with eight attention heads produces an F1 score of 91.497% for the testing set. The optimized vision transformer reduces the inference latency by 40.70%, measured through a batch-processing approach, with a 55.63% faster training time than the baseline. Lastly, we developed an enhanced skip-frame approach to improve the inference latency by finding an optimal ratio of frames for prediction at inference, where we could further reduce the inference latency by 57.15%. This study reveals that the vision transformer model is highly optimizable for inference latency while maintaining the model performance.

show abstract

Section: Discussionsupporting

confidence: 80%

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

Sarraf,

Kabia

2023

MAKE

View full text Add to dashboard Cite

show abstract

“…Constraint (6) demands that the distance between the location of a cluster head and the event is less than the sensing radius of a video sensor. Constraint (7) guarantees that the probability of the channel being idle must be larger than a given channel availability threshold. Constraint (8) ensures that a chosen cluster head and the sink should be located on the same side of the event movement locus, where (x 0 , y 0 ) is the location of event e whose moving direction is β and (x i , y i ) is the coordinate of v i .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Such networks can process, transmit and fuse multimedia data originated from heterogeneous sources. Quality-of-Service (QoS) sensitive video applications require more spectrum and transmission resources [2][3][4][5][6][7]. Cognitive radio (CR) [8] as a promising technology has been presented to improve spectrum utilization.…”

Section: Introductionmentioning

confidence: 99%

CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks

Shen

Wang

et al. 2019

Entropy

View full text Add to dashboard Cite

Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes.

show abstract

Optimization of Network Transmission of Multimedia Data Stream in a Cloud System

Izvozchikova

Tokareva

Shardakov

2021

Advances in Intelligent Systems, Computer Science and Digital Economics II

View full text Add to dashboard Cite

A real-time multimedia streaming transmission control mechanism based on edge cloud computing and opportunistic approximation optimization

Cited by 9 publications

References 20 publications

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

Optimal Topology of Vision Transformer for Real-Time Video Action Recognition in an End-To-End Cloud Solution

CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks

Optimization of Network Transmission of Multimedia Data Stream in a Cloud System

Contact Info

Product

Resources

About