Viettel Military Industry and Telecoms Group patented a system for live video streaming over tactical wireless networks. The system uses a multilayer perceptron neural network to estimate video distortion and adapt parameters based on network conditions. It includes a video motion estimator, bitrate optimizer, and neural network-based distortion estimator. GlobalData’s report on Viettel Military Industry and Telecoms Group gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Viettel Military Industry and Telecoms Group, Drone gimbals was a key innovation area identified from patents. Viettel Military Industry and Telecoms Group's grant share as of January 2024 was 40%. Grant share is based on the ratio of number of grants to total number of patents.
Adaptive live video streaming system based on distortion estimation
A recently granted patent (Publication Number: US11871047B2) discloses a system and methods for adaptive live video streaming based on distortion estimation using a multilayer perceptron neural network (MLP neural network). The system includes a video motion and detail estimator that analyzes video frames from a camera device to calculate the motion and detail level using specific formulae. Additionally, the methods for adaptive live streaming with channel condition involve measuring packet error rate and channel bandwidth at the receiver side, eliminating data outliers, estimating packet error rate and bandwidth using weighted average formulae, calculating motion and detail level of video frames, adjusting video bitrate and redundancy levels based on packet error rate, channel bandwidth, and video motion and detail level.
Furthermore, the patent details a method for creating a multilayer perceptron neural network-based distortion estimator through implementing the video streaming system over a simulated wireless channel. The simulation involves testing various redundancy configurations, video bitrates, channel conditions, and encoder settings to calculate distortion values using specific formulae. The system and methods aim to optimize video streaming quality by dynamically adjusting bitrate and redundancy levels based on real-time channel conditions and video characteristics, ultimately enhancing the viewing experience for users. The use of a neural network for distortion estimation allows for efficient decision-making in selecting optimal video bitrate and redundancy configurations, ensuring an optimal balance between video quality and bandwidth utilization in live streaming scenarios.
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