Image Processing and Restriction of Video Downloads Using Cloud

Krishna, Yaram Hari; Kumar, K B Shiva; Maharshi, Dasari; Amudhavel, J.

doi:10.14419/ijet.v7i2.32.15705

Cited by 4 publications

(1 citation statement)

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“…The main advantage is that it does not require any change in the underlying network layer to support streaming. The standard organization Moving Picture Experts Group (MPEG) and the 3rd Generation Partnership Project (3GPP) have standardized a method called Dynamic Adaptive Streaming over HTTP (DASH) to ensure interoperability [ 1 ]. In DASH implementation, the video is segmented, and each segment is stored with different video quality parameters, including spatial and temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Kumar

Raj

Jyothi

et al. 2022

Sensors

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Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client–server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

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Section: Introductionmentioning

confidence: 99%