TAROT: Towards Optimization-Driven Adaptive FEC Parameter Tuning for Video Streaming

TL;DR

TAROT introduces an adaptive, optimization-driven FEC controller for video streaming that dynamically tunes parameters per segment, significantly reducing overhead and improving quality across various network conditions.

Contribution

It presents a novel cross-layer FEC tuning method that is codec-agnostic, evaluates multiple parameters using a scoring model, and extends simulation tools for realistic testing.

Findings

Reduces FEC overhead by up to 43%.

Improves perceptual quality by 10 VMAF units.

Maintains minimal rebuffering across tests.

Abstract

Forward Error Correction (FEC) remains essential for protecting video streaming against packet loss, yet most real deployments still rely on static, coarse-grained configurations that cannot react to rapid shifts in loss rate, goodput, or client buffer levels. These rigid settings often create inefficiencies: unnecessary redundancy that suppresses throughput during stable periods, and insufficient protection during bursty losses, especially when shallow buffers and oversized blocks increase stall risk. To address these challenges, we present TAROT, a cross-layer, optimization-driven FEC controller that selects redundancy, block size, and symbolization on a per-segment basis. TAROT is codec-agnostic--supporting Reed-Solomon, RaptorQ, and XOR-based codes--and evaluates a pre-computed candidate set using a fine-grained scoring model. The scoring function jointly incorporates transport-layer loss and goodput, application layer buffer dynamics, and block-level timing constraints to penalize insufficient coverage, excessive overhead, and slow block completion. To enable realistic testing, we extend the SABRE simulator 1 with two new modules: a high-fidelity packet-loss generator that replays diverse multi-trace loss patterns, and a modular FEC benchmarking layer supporting arbitrary code/parameter combinations. Across Low-Latency Live (LLL) and Video-on-Demand (VoD) streaming modes, diverse network traces, and multiple ABR algorithms, TAROT reduces FEC overhead by up to 43% while improving perceptual quality by 10 VMAF units with minimal rebuffering, achieving a stronger overhead-quality balance than static FECs.