Edge Correction Model for A/V Stream Quality
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Solution Overview
Problem
Users experience poor stream quality due to insufficient bandwidth, hardware, or software limitations, leading to issues like buffering, lag, and reduced resolution/frame rate, with existing solutions often compromising quality by reducing bandwidth requirements.
Innovation Solution
Identifying edge processing devices with sufficient computing resources and using a correction model, such as a generative adversarial network (GAN), to enhance the audio/video stream quality by improving resolution and frame rate before transmission to the receiving device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bandwidth is reduced to accommodate limited network capacity, then streaming service becomes accessible to more users, but stream quality deteriorates with lower resolution and frame rate
Solution Approach 1:
A correction model is introduced as an intermediary component between the stream source and the client device. This correction model processes the incoming stream, corrects quality issues such as low resolution and frame rate, and outputs an enhanced stream to the client device, thereby maintaining stream quality without requiring increased bandwidth
Solution Approach 2:
The system changes the quality parameters of the stream locally at the client device or edge processing point by applying correction models. These models transform low-quality stream parameters (resolution, frame rate) into high-quality parameters without altering the original bandwidth requirements of the transmission
2Manufacturing precision
If client device hardware is upgraded to improve stream quality, then resolution and frame rate improve, but device cost and complexity increase
Solution Approach 1:
The correction model acts as a software intermediary that compensates for insufficient client device hardware capabilities. Instead of requiring powerful local hardware for real-time video processing, the correction model performs the enhancement tasks, allowing clients with modest hardware to still receive high-quality streams
Solution Approach 2:
The patent replaces the need for sophisticated mechanical/hardware processing systems in client devices with a software-based correction model. This substitution allows quality enhancement to be achieved through algorithmic processing rather than relying on expensive hardware components
3Manufacturing precision
If cloud processing is used to correct stream quality, then stream quality improves, but processing delay increases
Solution Approach 1:
The correction model is deployed locally at the edge or client device rather than being centralized in the cloud. This local deployment enables quality correction to occur in proximity to the user, minimizing transmission delays and enabling real-time or near-real-time stream enhancement without the latency associated with round-trip cloud processing
Data Source
AI summary
Aspects of the present disclosure relate to stream quality enhancement. A determination can be made that quality of an audio/video (A/V) stream being streamed to a first device falls below a quality threshold. A plurality of edge processing devices in an environment of the first device can be identified. Available computing resources of each of the edge processing devices can be determined. At least one edge processing device of the plurality of edge processing devices with sufficient computing resources for correcting quality of the A/V stream using a correction model can be selected. The selected at least one edge processing device can be instructed to correct the A/V stream using the correction model to generate a corrected A/V stream that satisfies the quality threshold. A command to transmit the corrected A/V stream to the first device can be issued.