This article details Bilibili's third‑generation streaming transcoding system, explaining its architecture, core modules, testing strategies, quality‑assurance plans, and code examples that together dramatically reduce processing time and support millions of daily video uploads.
This article examines the factors affecting media transcoding duration, explains why decoding dominates processing time, and presents multi‑threaded FFmpeg techniques and a cluster‑based architecture that split video into TS segments to dramatically reduce overall transcoding latency.
This article explains what XXL‑JOB is, why a distributed task‑scheduling platform is needed, and provides a step‑by‑step tutorial—including Docker deployment, Spring Boot integration, sharding, idempotency handling, and a video‑transcoding use case—to help developers implement reliable distributed jobs in Java applications.
Live streaming faces challenges due to real‑time video transmission and heavy computation, but by using globally distributed edge servers, transcoding streams into multiple resolutions, segmenting them into short clips, packaging them into formats like HLS, caching via CDNs, and optionally storing them in the cloud, the process delivers video seamlessly to viewers and enables replay.
This article describes the motivations, architecture, and key techniques of a custom video transcoding core built on FFmpeg, covering modular pipeline design, controllable serial/parallel execution, dynamic resolution and frame‑rate adaptation, SEI handling, and performance improvements for both live and on‑demand streaming.
The team built a custom video‑transcoding core atop FFmpeg libraries, replacing the command‑line tool with modular controllers, pipelines, and parallel tasks that dynamically adapt resolution, frame‑rate, and SEI handling for both low‑latency live streams and high‑throughput VOD, improving scalability and maintainability.
This article analyzes the fundamental goals of video transcoding, presents an information‑theoretic framework for bitrate reduction, compares traditional and deep‑learning codecs, and shares Bilibili's practical system design, parameter‑decision strategies, and visual‑quality‑aware optimizations that dramatically lower bandwidth consumption.
This article introduces the open‑source XXL‑JOB distributed task scheduling platform, explains how to install it via Docker, configure it in a Spring Boot application, and demonstrates a real‑world video‑transcoding use case with sharding, idempotency, and task‑status management.
The article recaps Tubi’s eighth Elixir Meetup, detailing Horvo’s presentation on building a live HLS video streaming backend with Elixir, covering the system’s transcoder, scheduler, HLS server, encryption, ad‑insertion strategies, and why Elixir was chosen for high‑performance backend services.
To meet Bilibili’s demanding live and on‑demand video transcoding needs, the BANG engine combines a multi‑stage pipeline architecture, frame‑block and multi‑frame parallelism, SIMD‑based CPU acceleration, and TensorRT/TensorFlow GPU inference, offering configurable string‑based pipelines that dramatically increase throughput while simplifying integration.
This article explores the rapid growth of video traffic, explains why transcoding is essential, compares CPU, GPU, and VPU hardware for video processing, details the FFmpeg software stack, describes the design of a cloud‑native transcoding cluster, its scheduling, shard‑transcoding technique, and presents performance test results.
A visual‑lossless deep‑learning pre‑processor that employs a DCT‑based low‑rank loss and an ultra‑lightweight CPU‑friendly model achieves up to 20% bitrate reduction for 1080p videos while preserving high‑frequency details, enabling real‑time transcoding and bandwidth savings for popular content on Bilibili.
This guide explains how to use Alibaba Cloud Function Compute asynchronous tasks for large‑scale data analysis, database autonomous services, Kafka‑based ETL pipelines, and high‑performance video transcoding, highlighting architecture migration, cost reduction, deployment steps, and observable serverless task capabilities.
Bilibili’s quality‑controlled scene‑adaptive transcoding system automatically splits videos into shot‑level segments, predicts optimal encoding parameters with a deep‑learning model, applies two‑pass VMAF‑targeted encoding and ROI‑aware bitrate allocation, achieving stable visual quality, 99% accuracy, and roughly 15% bitrate reduction.
Kuaishou’s audio‑video team built a dedicated high‑quality transcoding tier for Jackie Chan’s debut on the platform, deploying AI‑driven analysis, enhancement and compression techniques to deliver ultra‑clear short videos while keeping cost and latency low, and describing the end‑to‑end processing pipeline and trigger mechanisms.
Kuashou’s audio‑video team built a custom short‑video transcoding tier that combines AI‑driven enhancement, aggressive compression and a specialized delivery strategy to meet billions of viewers’ high‑quality expectations for Jackie Chan’s debut, showcasing rapid cross‑team collaboration and advanced video processing techniques.
At the 2021 Tencent Digital Ecology Conference, senior executives unveiled three home‑grown chips—ZiXiao for AI inference, CangHai for video transcoding, and XuanLing for high‑performance networking—detailing their 2.5D HBM2e integration, accelerated codecs, and up to four‑fold performance improvements that position Tencent’s custom silicon as a core infrastructure for cloud services.
This article analyzes the latency of video publishing in a mobile app, proposes a parallel video‑track transcoding solution, details implementation steps, performance testing, and optimization techniques to reduce encoding time by up to 30% on Android devices.
NetEase Cloud’s video transcoding service boosts processing speed by combining hardware acceleration, custom codecs, AMD EPYC servers, and a slice‑based parallel transcoding pipeline, while optimizing cluster task scheduling and handling straggler issues to achieve significant performance gains across large‑scale media workloads.
This article examines the technical challenges of massive e‑commerce live streams, explains why traditional streaming suffers from high latency, and details Huawei Cloud's low‑latency architecture, protocols, intelligent scheduling and ultra‑fast transcoding that enable sub‑second product link delivery to millions of viewers.
The article details Tubi's ad‑serving pipeline from pacing control through bid collection, video transcoding, CPU‑intensive bidding optimization, result rendering, and final integration, highlighting the use of Akka, Spark, CRDTs, and gRPC in a distributed backend architecture.
This article explains the fundamentals of video streaming, media transcoding, and CDN technology, then details Alibaba Cloud's live streaming architecture, including terminology, workflow stages, system components, business functions, and typical application scenarios such as UGC, e‑commerce, sports, gaming, and online education.
This article explains how to design and implement an elastic, high‑availability video‑on‑demand solution on Alibaba Cloud using Function Compute and Function Flow, covering simple and full‑featured architectures, performance and cost comparisons, and practical deployment steps.
This article explains the design of a distributed video transcoding platform that leverages AWS Lambda and EC2‑SLAVE to achieve high‑concurrency, low‑latency streaming, detailing architecture, load balancing, health checks, task monitoring, and cost‑saving strategies for scalable cloud‑based video processing.
Tencent's cloud editing system transforms live‑stream post‑production by ingesting parallel low‑ and high‑bitrate streams, enabling real‑time, frame‑precise cuts, fuzzy seeking, and WYSIWYG effects via HLS and NPAPI, while eliminating traditional VOD transcoding delays and paving the way toward a full‑featured online editing platform.
This article explains the fundamentals of video streaming, media transcoding, and CDN technologies, then details Alibaba Cloud's live streaming architecture—including capture, preprocessing, encoding, push‑stream, transcoding, distribution, and client playback—along with business functions, monitoring, and typical application scenarios.
