VineetVC: Adaptive Video Conferencing Under Severe Bandwidth Constraints Using Audio-Driven Talking-Head Reconstruction

TL;DR

This paper presents an adaptive video conferencing system that uses audio-driven talking-head reconstruction to maintain communication quality under severe bandwidth constraints, integrating WebRTC, telemetry, and AI-based synthesis.

Contribution

It introduces a novel adaptive system combining WebRTC, telemetry, and AI synthesis to reduce bandwidth usage while preserving video communication quality.

Findings

Median bandwidth for synthesized stream is 32.80 kbps.

Bandwidth-mode switching improves stability under constraints.

Client-side mode-state logging aids in system analysis.

Abstract

Intense bandwidth depletion within consumer and constrained networks has the potential to undermine the stability of real-time video conferencing: encoder rate management becomes saturated, packet loss escalates, frame rates deteriorate, and end-to-end latency significantly increases. This work delineates an adaptive conferencing system that integrates WebRTC media delivery with a supplementary audio-driven talking-head reconstruction pathway and telemetry-driven mode regulation. The system consists of a WebSocket signaling service, an optional SFU for multi-party transmission, a browser client capable of real-time WebRTC statistics extraction and CSV telemetry export, and an AI REST service that processes a reference face image and recorded audio to produce a synthesized MP4; the browser can substitute its outbound camera track with the synthesized stream with a median bandwidth of 32.80 kbps. The solution incorporates a bandwidth-mode switching strategy and a client-side mode-state logger.