LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models We present LLM-ABR, the first system that utilizes the gen-erative capabilities of large language models (LLMs) to au-tonomously design adaptive bitrate (ABR) algorithms tai-lored for diverse network characteristics
A Survey On Adaptive Bitrate Algorithms and Their Improvisations The ABR algorithms are based on fixed rule based logic and are specific to the device environment on which they are supposed to be deployed Our proposed paper discusses the existing ABR algorithms and provides a comparative study of their performance with respect to QoE improvisations
From Theory to Practice: Improving Bitrate Adaptation in the DASH . . . To maximize the quality-of-experience (QoE) of the user, ABR algorithms must stream at a high bitrate with low rebuffering and low bitrate oscillations Further, a good ABR algorithm is responsive to user and network events and can be used in demanding scenarios such as low-latency live streaming
Adaptive Bitrate Selection: A Survey - ResearchGate The rate adaptation controller of HAS, commonly called Adaptive Bitrate Selection (ABR), is currently receiving a lot of attention from both industry and academia
Bitrate Adaptation and Guidance With Meta Reinforcement Learning Abstract: Adaptive bitrate (ABR) schemes enable streaming clients to adapt to time-varying network device conditions for a stall-free viewing experience Most ABR schemes use manually tuned heuristics or learning-based methods
Experimenting with Adaptive Bitrate Algorithms for Virtual Reality . . . In this paper, we experiment with ABR algorithms for VR streaming using Air Light VR (ALVR), an open-source VR streaming solution We extend ALVR with a comprehensive set of metrics that provide a robust characterization of the network state, enabling more informed bitrate adjustments