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.

翻译：前向纠错（FEC）对于保护视频流媒体免受丢包影响仍然至关重要，然而大多数实际部署仍依赖于静态、粗粒度的配置，无法对丢包率、有效吞吐量或客户端缓冲区水平的快速变化作出响应。这些僵化的设置常常导致效率低下：稳定时期不必要的冗余抑制了吞吐量，而在突发丢包期间（特别是当浅缓冲区和过大的数据块增加了卡顿风险时）保护又不足。为应对这些挑战，我们提出了TAROT，一种跨层、优化驱动的FEC控制器，它能够基于每个视频片段动态选择冗余度、块大小和符号化方案。TAROT与编解码器无关——支持Reed-Solomon、RaptorQ和基于XOR的编码——并通过细粒度的评分模型评估预计算的候选参数集。该评分函数综合考虑了传输层丢包与有效吞吐量、应用层缓冲区动态以及块级时序约束，以惩罚覆盖不足、开销过大和块完成过慢的情况。为实现真实测试，我们扩展了SABRE模拟器，新增两个模块：一个高保真丢包生成器，可复现多样化的多轨迹丢包模式；以及一个模块化的FEC基准测试层，支持任意的编码/参数组合。在低延迟直播（LLL）和视频点播（VoD）流媒体模式、多样化的网络轨迹以及多种自适应码率（ABR）算法下，TAROT将FEC开销降低了高达43%，同时将感知质量提升了10个VMAF单位，且重缓冲极少，相比静态FEC实现了更优的开销-质量平衡。