Edge AI-Based Image Compression and Transmission Optimization for High-Resolution Video Streaming over 5G Networks

Abstract

The present invention discloses an edge AI-based image compression and transmission optimization system for high-resolution video streaming over 5G networks. The system includes a content-aware compression module, a network condition monitoring module, and a transmission optimization module. By leveraging AI at the network edge, the system dynamically adjusts video compression and transmission parameters based on real-time network conditions, content complexity, and user preferences. This approach enables efficient delivery of high-quality video with minimal latency, supporting applications in areas such as live broadcasting, telemedicine, and remote monitoring. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The present invention pertains to the fields of edge computing, artificial intelligence, video streaming, and telecommunications. More specifically, it relates to the use of AI at the network edge to perform image compression and transmission optimization for high-resolution video streaming over 5G networks. This invention addresses challenges in delivering real-time, high-quality video content by leveraging the computational capabilities at the network edge, thereby reducing latency and optimizing bandwidth utilization.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] With the advent of 5G networks, real-time streaming of high-resolution video content has become feasible across diverse applications, including entertainment, telemedicine, and remote monitoring. However, high-resolution video streams generate large amounts of data, demanding efficient compression and transmission strategies to ensure smooth delivery without overwhelming network resources. Traditional compression techniques often degrade video quality, especially when applied to high-resolution content. Additionally, existing methods may not effectively adjust to varying network conditions, leading to buffering, latency, and degraded user experiences.
[004] Edge AI, which utilizes AI algorithms directly at the network edge, offers a promising solution by performing computationally intensive tasks such as image compression closer to the data source. This approach enables real-time adjustments to video quality and transmission parameters based on available bandwidth, network conditions, and content complexity. This invention introduces an edge AI-based framework for adaptive compression and transmission optimization, allowing high-quality, latency-free video streaming over 5G networks.
[005] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides a Hybrid Machine Learning System for Noise Reduction and Super Resolution in Biomedical Imaging and method thereof. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[006] This invention provides an edge AI-based image compression and transmission optimization system designed to improve the efficiency of high-resolution video streaming over 5G networks. The system employs a series of machine learning models deployed at the network edge to dynamically adjust video compression levels, data rates, and other transmission parameters in real-time. By analyzing network conditions, video content complexity, and user requirements, the system reduces data volume while maintaining visual quality.
[007] The system comprises three main modules: (1) a content-aware compression module that uses AI to determine optimal compression levels for each video frame, (2) a network condition monitoring module that assesses 5G network bandwidth and latency in real-time, and (3) a transmission optimization module that adjusts data transmission based on network conditions and user preferences. This edge-based approach allows the system to compress and transmit high-quality video with minimal latency, supporting a range of applications from live broadcasts to remote telemedicine.
[008] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[009] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.


BRIEF DESCRIPTION OF THE DRAWINGS
[010] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
FIG. 1: Block diagram of the edge AI-based image compression and transmission optimization system architecture.
FIG. 2: Flowchart showing the process of content-aware compression, detailing the AI-based decision-making for compression levels.
FIG. 3: Diagram of the network condition monitoring module, illustrating real-time tracking of bandwidth and latency over 5G networks.
FIG. 4: Flowchart of the transmission optimization module, showing the steps for adjusting transmission parameters based on network conditions.
FIG. 5: Example of optimized video frames before and after compression, demonstrating the system's ability to maintain high visual quality at lower data rates.

DETAILED DESCRIPTION OF THE INVENTION
[011] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[012] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[013] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
System Architecture (FIG. 1)
[014] The system architecture consists of three main modules: a content-aware compression module, a network condition monitoring module, and a transmission optimization module. Each of these modules is deployed at the network edge, enabling low-latency processing and rapid adjustments to video compression and transmission settings based on real-time data.
[015] Content-Aware Compression Module (FIG. 2): This module uses machine learning algorithms, such as convolutional neural networks (CNNs) or encoder-decoder architectures, to analyze each video frame in real time and determine the optimal compression level.
[016] The module considers factors such as motion, texture, and spatial complexity within the video content to apply appropriate compression techniques. For high-motion scenes, the system may apply less compression to preserve quality, while static or low-detail scenes can be compressed more heavily to reduce data rates. This adaptive compression strategy minimizes data usage without sacrificing image quality.
[017] Network Condition Monitoring Module (FIG. 3): The network condition monitoring module continuously tracks the bandwidth, latency, and packet loss over the 5G network. Using these metrics, the system can predict changes in network conditions and anticipate bandwidth availability, ensuring stable video streaming.
[018] This module integrates a predictive model trained on historical network performance data, allowing it to proactively adjust transmission settings in anticipation of network fluctuations.
[019] Transmission Optimization Module (FIG. 4): The transmission optimization module dynamically adjusts video data rates, frame rates, and resolution based on the network conditions observed by the monitoring module. When network bandwidth is high, the module maintains full-resolution, high-frame-rate streaming, whereas during low bandwidth periods, it reduces the resolution or frame rate to prevent buffering.
[020] This module also supports user-configurable settings, enabling users to prioritize either video quality or data savings based on their preferences. For instance, users in remote areas with limited connectivity can opt for lower resolution to maintain continuous streaming.
[021] Edge-Based Real-Time Processing: The edge AI framework minimizes latency by processing compression and transmission adjustments at the network edge, closer to the data source. This approach reduces the need to send large amounts of video data to a central server for processing, thereby optimizing overall transmission efficiency.
[022] Output and Quality Assurance (FIG. 5): The optimized video frames are transmitted in real time to the user device. The system ensures high visual quality by maintaining low compression levels on critical content areas, such as faces in a telemedicine call or text details in a live presentation.
[023] A feedback loop periodically assesses user satisfaction with the video quality, enabling further tuning of the system's parameters to match user expectations.
Workflow
[024] Video Data Ingestion and Preprocessing: The input video is ingested at the network edge, where initial preprocessing steps standardize frame sizes and resolutions, optimizing them for downstream processing by the content-aware compression module.
[025] Content-Aware Compression Process: The compression module analyzes each frame's content characteristics and applies a variable compression rate based on content complexity. High-detail frames are minimally compressed, while low-detail frames are compressed at higher rates to reduce data requirements.
[026] Network Condition Monitoring: This module gathers network data such as bandwidth and latency and uses predictive models to anticipate changes in network conditions. Based on these predictions, the system proactively adjusts video compression and transmission settings to prevent streaming interruptions.
[027] Transmission Optimization: The transmission optimization module adjusts video data rates, frame rates, and resolutions in response to the monitored network conditions, ensuring continuous streaming with minimal buffering.
[028] Real-Time Feedback Loop: The system incorporates user feedback and quality metrics to fine-tune video streaming parameters continuously. This feedback loop enhances video quality by adjusting parameters based on actual user satisfaction and network performance.
[029] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[030] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[031] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. An edge AI-based image compression and transmission optimization system for high-resolution video streaming over 5G networks, comprising a content-aware compression module, a network condition monitoring module, and a transmission optimization module.
2. The system of claim 1, wherein the content-aware compression module applies machine learning algorithms to dynamically adjust compression levels based on video content complexity.
3. The system of claim 1, wherein the network condition monitoring module tracks bandwidth, latency, and packet loss over the 5G network to predict network fluctuations.
4. The system of claim 1, wherein the transmission optimization module adjusts video data rates, frame rates, and resolutions in response to network conditions to ensure continuous streaming without buffering.
5. The system of claim 1, wherein the compression and transmission optimization processes are performed at the network edge to minimize latency and reduce data transmission to central servers.
6. The system of claim 2, wherein the content-aware compression module utilizes convolutional neural networks to analyze frame complexity and apply compression based on motion, texture, and spatial characteristics.
7. The system of claim 1, further comprising a feedback loop that incorporates user satisfaction data to adjust video quality parameters continuously.
8. The system of claim 1, wherein the transmission optimization module allows users to configure preferences for video quality versus data savings.

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