IBM Cloud Video

Description

IBM Cloud Video is an end-to-end platform providing seamless live and on-demand video content delivery across multiple platforms, including employee communications and virtual conferences. With advanced AI capabilities, it enhances viewer engagement and optimizes video performance in real time.

Resource details

Category path
Infrastructure & Delivery
Classification
Not yet classified

IBM Cloud Video is cataloged in Infrastructure & Delivery. Its topics include video streaming, cloud video, enterprise video, AI, and video platform.

URL

Tags

Related resources

  • HR-Cache: Learning-Based Caching for Edge Content DeliveryAcademic paper proposing a LightGBM GBDT-based learned cache-eviction model for edge content delivery, with companion code.
  • Improving Hierarchy Storage for Video Streaming in CloudAcademic paper proposing an algorithm to tier video segments by access frequency across cloud storage classes for streaming efficiency.
  • Open Streaming PlatformSelf-hosted live streaming platform (Twitch/Ustream alternative) with RTMP ingest, Flask + nginx-rtmp-module stack.
  • ClipableSelf-hosted video sharing platform, a Streamable alternative for cloud/self-managed video hosting.
  • live-streaming-server-net.NET RTMP live streaming server with HTTP-FLV/WebSocket-FLV/HLS output, Kubernetes and cloud storage integration, admin panel.
  • Build a Self-Hosted CDN with OpenResty EdgeStep-by-step guide to building a self-hosted CDN using OpenResty Edge (LuaJIT edge gateway), from the OpenResty core team.
  • OpencastOpen-source platform for capturing, processing, managing and distributing academic video, with its own storage and distribution architectur…
  • jsDelivrProduction multi-CDN (Cloudflare+Fastly) delivery network with RUM+synthetic monitoring load balancer and dual-DNS failover architecture, o…
  • RustFSRust S3-compatible object storage claiming 2.3x MinIO performance on small-payload workloads, drop-in migration from MinIO/Ceph, good fit f…
  • ML-Driven Open-Source Framework for QoE in Multimedia NetworksEnd-to-end ML framework predicting and optimizing QoE from network conditions, based on ITU-T P.1203, code released open source.
  • Survive CDN Failures with Redundant StreamsTechnical deep-dive on client-side multi-CDN failover pattern using redundant streams with HLS.js.