Towards Scalable, Low-Latency Volumetric Streaming: A Hybrid Predictive Synchronization Framework

Abstract

Volumetric video is emerging as a cornerstone for multi-user Extended Reality (XR) and metaverse applications. However, achieving synchronous playback across distributed clients remains challenging under heterogeneous network conditions (such as varying latency, jitter, packet loss, and asymmetric bandwidth). Existing synchronization approaches, such as state synchronization and fixed-frame buffering, struggle with scalability and often trade off latency for playback continuity. We present a predictive synchronization framework that combines lightweight time-series latency forecasting with adaptive buffering control. Our framework incorporates a hybrid predictor that adaptively selects the most suitable model (including EWMA, ARIMA, LSTM) for each client, escalating to heavier predictors only when residual errors exceed thresholds. This design balances accuracy with computational overhead, enabling lower latencies at larger scales. Our experimental results show that predictive synchronization reduces average inter-client skew by up to 76% compared to baselines, while also decreasing the average buffer size by 40% in a controlled multi-client testbed. The average per-frame latency remains below 20 ms and the 95th-percentile latency below 70 ms for up to 100 concurrent clients. These results demonstrate that prediction-aware, hybrid synchronization substantially improves quality of service while maintaining lightweight per-client overhead.