Fastly Compute LL-HLS
Description
Fastly Compute@Edge application for LL-HLS playlist manipulation and request collapsing at the edge for sub-second segment delivery.
Resource details
- Category path
- Protocols & Transport›Low-Latency Streaming Tools
- Classification
- Not yet classified
Fastly Compute LL-HLS is cataloged in Protocols & Transport and Low-Latency Streaming Tools.
URL
Related resources
- Ultra-low Latency Video Delivery Over WebRTC Data ChannelsMile-High Video 2024 paper on achieving ultra-low latency video delivery using WebRTC data channels.
- Point Cloud Streaming with Latency-Driven Implicit Adaptation using MoQ2025 paper applying Media-over-QUIC (MoQ) transport to low-latency adaptive point-cloud streaming.
- Harnessing WebRTC for Large-Scale Live StreamingACM SIGCOMM 2025 paper on scaling WebRTC for large-audience low-latency live streaming.
- Performance of Low-Latency HTTP-based Streaming PlayersMMSys'21 paper comparing AVPlayer, Shaka, HLS.js, and Dash.js performance under LL-HLS and LL-DASH.
- Low Latency Live Streaming Implementation in DASH and HLSACM MM'22 paper (Bentaleb et al.) on low-latency live streaming implementation, code contributions merged into hls.js.
- KTH Thesis: Interoperable Retransmission Protocols for Low-Latency Video (RIST/SRT)Master's thesis academic evaluation comparing RIST and SRT retransmission protocols for low-latency video contribution.
- The Analysis of DASH Manifest OptimizationsAmazon-authored ACM Green Multimedia Systems workshop paper analyzing MPD manifest size optimization techniques.
- PantomimeSwift M3U8 parser supporting both master and media playlists.
- low-latency-dash-ffmpegDockerized ffmpeg reference configuration for LL-DASH per-frame moof chunk streaming.
- FOSDEM 2024: Streaming Live Using RIST On Demand to ThousandsFOSDEM Open Media devroom talk on a real-world RIST architecture achieving 500ms glass-to-glass latency at scale, comparing RIST vs QUIC.