Real-time video streaming is now one of the main applications in all network environments.
Due to the fluctuation of throughput under various network conditions, how to choose a …

In this paper, we introduce a continuous bitrate control and latency control model for the Live
Video Streaming Challenge. Our model is based on Deep Deterministic Policy Gradient …

Currently, video content accounts for the majority of network traffic. With increased live
streaming, rigorous requirements have been introduced for better Quality of Experience …

A large fraction of video content providers have adopted adaptive bitrate streaming over
HTTP. The client player typically runs an adaptive bitrate (ABR) algorithm to decide upon the …

Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically
changing network conditions to ensure a high quality of experience (QoE) for the users …

Improving the overall Quality of Experience (QoE) of a client for a given video streaming
session is a challenging task in a continuously varying network environment. A dynamic bit …

Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It
poses significant challenges for streaming over the Internet. In this paper, we explore the …

Contemporary real-time video communication systems, such as WebRTC, use an adaptive
bitrate (ABR) algorithm to assure high-quality and low-delay services, eg, promptly adjusting …

Rate adaptation is widely adopted in video streaming to improve the quality of experience
(QoE). However, most of the existing rate adaptation approaches neglect the underlying …

In the typical transcoding pipeline for adaptive video streaming, raw videos are pre-chunked
and pre-encoded according to a set of resolution-bitrate or resolution-quality pairs on the …