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Low-Latency Video Streaming: Applications, Challenges, and Trends
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DOI：10.1109/COMST.2026.3689266.png)
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Abstract
En 中文
The demand for low-latency video streaming has grown rapidly with the emergence of mission-critical and highly interactive applications such as extended reality (XR), remote surgery, cloud gaming, teleoperation, and autonomous systems. Despite continuing advances in networking and computing technologies, achieving consistently low end-to-end delay while maintaining service quality remains a fundamental challenge. Existing surveys often focus on individual components of the streaming pipeline, whereas a unified and up-to-date view of low-latency Internet video streaming is still lacking. This survey addresses this gap by providing a structured review of low-latency Internet video streaming from the perspectives of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">applications</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">core challenges</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">future trends</i>. We first summarize representative application domains and clarify how their latency requirements motivate different technical bottlenecks. We then organize the literature around three recurring challenges: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Large Data</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Network Bandwidth</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prediction</i>, covering representative solutions in video compression and delivery, transport and congestion control, adaptive streaming, and QoS/QoE prediction and evaluation. In addition, we review how recent AI techniques are reshaping the low-latency streaming pipeline, including learning-based optimization, generative/foundation-model-enabled streaming, and intelligent network control. By synthesizing representative techniques, distilling lessons learned, and highlighting open challenges and research directions, this survey provides a unified and forward-looking reference for researchers and practitioners working on next-generation low-latency video streaming systems.
Keywords:
Low-latency video streaming
adaptive streaming
transport protocols
video compression
QoE
artificial intelligence
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