Embedded Codebooks for Client-Side Media Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud-based digital media service providers face challenges in managing the increasing processing demands for encoding and decoding digital media, leading to resource strain on servers and high bandwidth consumption, while also needing to comply with copyright laws and jurisdictional requirements.
Innovation Solution
The solution involves offloading resource-intensive decoding tasks to client devices by generating and embedding codebooks into streaming transmissions, allowing client devices to decode encoded content locally, thereby optimizing server resources and reducing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the server streams decoded content to the client device, then the client device can easily play the content, but the server consumes excessive processing power and bandwidth
Solution Approach 1:
The patent extracts the decoding function from the server and relocates it to the client device. The server only transmits encoded content and codebook data, while the client device performs the decoding operation locally using the provided codebook, thereby eliminating the need for the server to perform resource-intensive decoding operations.
Solution Approach 2:
The patent introduces a codebook as an intermediary element that enables the client device to decode content without requiring the server to perform decoding. The codebook serves as a reference that bridges the encoded content and the client's decoding capability, allowing the server to avoid direct involvement in the decoding process.
2Productivity
If the server stores codebooks for each client device, then decoding can be optimized for each client, but storage requirements increase
Solution Approach 1:
The patent applies local quality by generating and storing codebooks with specific characteristics tailored to individual client devices' capabilities (e.g., processing power, memory constraints, supported formats). Each client receives a codebook optimized for its specific hardware and software environment, improving decoding efficiency while maintaining personalized optimization.
Solution Approach 2:
The patent implements preliminary action by pre-generating codebooks on the server side before they are needed by the client devices. These pre-generated codebooks are stored in a codebook repository and retrieved when needed, eliminating the need for real-time codebook generation and reducing server processing demands during actual content delivery.
3Adaptability or versatility
If the server processes and encodes more digital media files, then more content is available to clients, but processing demands and resource strain increase
Solution Approach 1:
The patent applies segmentation by dividing the content delivery system into distinct functional components: the server handles content storage, encoding, and codebook generation, while the client device handles decoding and playback. This segmentation allows the server to focus on content management without being burdened by decoding operations, enabling it to handle larger volumes and varieties of content.
Solution Approach 2:
The patent implements self-service by enabling client devices to perform their own decoding operations using locally stored codebooks. This eliminates the need for the server to provide decoding services for each content request, allowing the server to scale content storage and encoding capabilities without proportionally increasing processing demands.
Data Source
AI summary
Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.