View source for 17955444. SYSTEM AND METHOD FOR ULTRA LOW LATENCY LIVE STREAMING BASED ON USER DATAGRAM PROTOCOL simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=SYSTEM AND METHOD FOR ULTRA LOW LATENCY LIVE STREAMING BASED ON USER DATAGRAM PROTOCOL=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Luckraj Shrawan Kumar of Bengaluru, Karnataka (IN)|Luckraj Shrawan Kumar of Bengaluru, Karnataka (IN)]][[Category:Luckraj Shrawan Kumar of Bengaluru, Karnataka (IN)]]

[[:Category:Gopinath Chennakeswaran of Bangalore (IN)|Gopinath Chennakeswaran of Bangalore (IN)]][[Category:Gopinath Chennakeswaran of Bangalore (IN)]]

[[:Category:Renuka Varry of Bangalore (IN)|Renuka Varry of Bangalore (IN)]][[Category:Renuka Varry of Bangalore (IN)]]

[[:Category:Divyanshu Chuchra of Bengaluru (IN)|Divyanshu Chuchra of Bengaluru (IN)]][[Category:Divyanshu Chuchra of Bengaluru (IN)]]

[[:Category:Umang Bhatia of Gurgaon (IN)|Umang Bhatia of Gurgaon (IN)]][[Category:Umang Bhatia of Gurgaon (IN)]]

[[:Category:Utkarsh Mohan of Patna, Bihar (IN)|Utkarsh Mohan of Patna, Bihar (IN)]][[Category:Utkarsh Mohan of Patna, Bihar (IN)]]

[[:Category:Aalok Kumar Gupta of Koderma, Jharkhand (IN)|Aalok Kumar Gupta of Koderma, Jharkhand (IN)]][[Category:Aalok Kumar Gupta of Koderma, Jharkhand (IN)]]

==SYSTEM AND METHOD FOR ULTRA LOW LATENCY LIVE STREAMING BASED ON USER DATAGRAM PROTOCOL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17955444 titled 'SYSTEM AND METHOD FOR ULTRA LOW LATENCY LIVE STREAMING BASED ON USER DATAGRAM PROTOCOL

==Simplified Explanation==

The abstract describes a method for enhancing video streaming by using neural networks. Here are the key points:

* The method involves training a client-side neural network (NN) in a client device using a client-side environment parameter.
* The client-side encoding bit rate is determined based on the training result and transmitted to a server-side neural network (NN) in a server device.
* The server-side NN is trained using the client-side encoding bit rate and a server-side environment parameter.
* The server-side encoding bit rate is determined based on the training result, and the encoding bit rate of the video stream is adjusted accordingly.

== Potential Applications ==
This technology can be applied in various video streaming scenarios, including:

* Online video platforms: Enhancing the video streaming experience for users by optimizing encoding bit rates based on client and server environments.
* Video conferencing: Improving the quality and stability of video calls by dynamically adjusting encoding bit rates.
* Live streaming: Optimizing video quality and reducing buffering issues during live broadcasts.

== Problems Solved ==
The method addresses the following problems in video streaming:

* Inconsistent video quality: By training neural networks based on client and server environments, the method ensures that the encoding bit rate is optimized for the specific conditions, resulting in a more consistent video quality.
* Bandwidth optimization: By adjusting the encoding bit rate based on the determined server-side encoding bit rate, the method helps optimize bandwidth usage and reduce buffering issues.
* Real-time adaptation: The method allows for real-time adjustments to the encoding bit rate, ensuring a smooth video streaming experience even in changing network conditions.

== Benefits ==
The use of neural networks in video streaming offers several benefits:

* Improved video quality: By training neural networks based on client and server environments, the method can enhance video quality by optimizing the encoding bit rate.
* Enhanced user experience: The real-time adjustments to the encoding bit rate help provide a smoother and more stable video streaming experience for users.
* Bandwidth efficiency: By dynamically adjusting the encoding bit rate, the method helps optimize bandwidth usage and reduce data consumption without compromising video quality.


==Original Abstract Submitted==

An example method includes for enhancing video streaming includes inputting a client-side environment parameter to a client-side neural network (NN) implemented in a client device and then training the client-side NN based on the inputted client-side environment parameter. Thereafter, a client-side encoding bit rate is determined based on a result of the training of the client-side NN and then transmitting the determined client-side encoding bit rate as an input to a server-side neural network (NN) implemented in a server device. The method further includes training the server-side NN based on the inputted client-side encoding bit rate and a server-side environment parameter. Then, a server-side encoding bit rate is determined based on a result of the training of the server-side NN and thereafter, an encoding bit rate of an encoder for the video stream is adjusted based on the determination of the server-side encoding bit rate, and the server-side environment parameter.



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