TAROT: Towards Optimization-Driven Adaptive FEC Parameter Tuning for Video Streaming
Jashanjot Singh Sidhu, Aman Sahu, Abdelhak Bentaleb

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…
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Taxonomy
TopicsNetwork Traffic and Congestion Control · Image and Video Quality Assessment · Peer-to-Peer Network Technologies
