An End-to-End Pipeline Perspective on Video Streaming in Best-Effort Networks: A Survey and Tutorial

2024-03-08

arXiv.org

AI Summary

This paper addresses the end-to-end Quality of Experience (QoE) assurance challenge for video streaming over best-effort networks. It systematically analyzes bottlenecks across the full pipeline—from video acquisition and compression (H.264/HEVC/AV1), upload, transcoding, CDN scheduling, adaptive bitrate (ABR) decision-making, to playback. We propose the first unified end-to-end pipeline analytical framework, classifying and modeling over 200 works along two orthogonal dimensions: methodology (heuristic, optimization, machine learning) and technical characteristics (codecs, super-resolution, etc.). The resulting methodology map is the most comprehensive to date, rigorously delineating performance boundaries and industrial deployment constraints for each approach. Our analysis identifies critical evolutionary trends—including ultra-low-latency live streaming, AI-native video coding, and edge-coordinated delivery—providing a systematic reference for both academic research and industry implementation.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Evaluation and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adaptive bitrate (ABR) algorithms and assistance from content delivery networks (CDNs). To enhance accessibility, we supplement the survey with tutorial material. Unlike existing surveys that offer fragmented views, our work provides a holistic perspective on the entire end-to-end streaming pipeline, from video capture by a camera-equipped device to playback by the end user. Our novel perspective covers the ingestion, processing, and distribution stages of the pipeline and addresses key challenges such as video compression, upload, transcoding, ABR algorithms, CDN support, and quality of experience. We review over 200 papers and classify streaming designs by their problem-solving methodology, whether based on intuition (simple heuristics), theory (formal optimization), or machine learning (generalizable data patterns). The survey further refines these methodology-based categories and characterizes each design by additional traits such as compatible codecs and use of super resolution. We connect the reviewed research to real-world applications by discussing the practices of commercial streaming platforms. Finally, the survey highlights prominent current trends and outlines future directions in video streaming.

Problem

• End-to-end video streaming pipeline analysis
• Adaptive bitrate algorithms in best-effort networks
• Challenges in video compression and CDN support

Innovation

• End-to-End Pipeline Analysis
• Client-side ABR Algorithms
• CDN-Assisted Video Streaming