Multi-Channel XR Video Rate Control with Adaptive Quantization
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Solution Overview
Problem
Cloud-based extended reality (XR) systems face challenges in optimizing network bandwidth utilization for multi-channel video streaming, leading to potential underutilization or overutilization, which affects user experience through lower quality frames or increased latency and packet loss.
Innovation Solution
Implementing a server-side multi-channel video rate control mechanism that adjusts quantization parameters (QPs) based on available network bandwidth, maintaining predefined relationships between QPs to ensure optimal encoding, thereby balancing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If additional encoded frames including depth data are generated to improve user experience, then frame quality is improved, but network bandwidth utilization increases leading to potential overutilization and packet loss
Solution Approach 1:
The system dynamically adjusts the quantization parameters (QPs) for encoding depth frames based on real-time network bandwidth conditions. When network capacity is sufficient, lower QP values are used to maintain high frame quality. When bandwidth is constrained, QP values are increased to reduce bandwidth consumption and prevent packet loss, thus adaptively balancing quality and reliability
Solution Approach 2:
The invention changes the encoding parameters (quantization parameters) of depth frames based on available network bandwidth. By adjusting these parameters, the system can control the bitrate of encoded frames to match network capacity, preventing both underutilization and overutilization of bandwidth while maintaining optimal frame quality
2Manufacturing precision
If network bandwidth utilization is increased to improve frame quality, then manufacturing precision is improved, but loss of information increases due to packet loss
Solution Approach 1:
The system implements a feedback mechanism that monitors network bandwidth utilization and adjusts encoding parameters accordingly. By continuously measuring actual bandwidth usage and comparing it against available capacity, the system can adjust quantization parameters in real-time to prevent overutilization and the resulting packet loss, ensuring information integrity
3Productivity
If quantization parameters are adjusted to optimize encoding, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary estimation of encoding output bits using reverse rate control before actual encoding. By predicting the bitrate requirements based on initial QP settings and frame characteristics, the system can pre-adjust parameters to match network capacity, improving encoding efficiency while avoiding the need for complex iterative optimization during real-time operation
Data Source
AI summary
A cloud-based extended reality (XR) system includes a server configured to encode a set of frames each associated with an XR scene to be displayed. To encode the set of frames, the server estimates a total number of encoded output bits for the set of frames based on a set of quantization parameters (QPs). The set of QPs includes a corresponding QP for each frame of the set of frames and one or more predetermined relationships between the corresponding QPs. The server then compares the estimated total number of encoded output bits to a target frame size threshold. Based on the estimated total number of encoded bits being outside the target frame size threshold, the server updates the set of QPs so as to maintain the predetermined relationships between the QPs.