mlsanityData to 14 Oct 2026

LL-GABR: Energy Efficient Live Video Streaming Using Reinforcement Learning

arXiv 2402.09392 ↗First posted 14 Feb 2024cs.MM cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

Over the recent years, research and development in adaptive bitrate (ABR) algorithms for live video streaming have been successful in improving users' quality of experience (QoE) by reducing latency to near real-time levels while delivering higher bitrate videos with minimal rebuffering time. However, the QoE models used by these ABR algorithms do not take into account that a large portion of live video streaming clients use mobile devices where a higher bitrate does not necessarily translate into higher perceived quality. Ignoring perceived quality results in playing videos at higher bitrates without a significant increase in perceptual video quality and becomes a burden for battery-constrained mobile devices due to higher energy consumption. In this paper, we propose LL-GABR, a deep reinforcement learning approach that models the QoE using perceived video quality instead of bitrate and uses energy consumption along with other metrics like latency, rebuffering events, and smoothness. LL-GABR makes no assumptions about the underlying video, environment, or network settings and can operate flexibly on different video titles, each having a different bitrate encoding ladder without additional re-training, unlike existing learning-based ABRs. Trace-driven experimental results show that LL-GABR outperforms the state-of-the-art approaches by up to 44% in terms of perceptual QoE and a 73% increase in energy efficiency as a result of reducing net energy consumption by 11%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 35 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

5 of 35 references.

References · 5

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2018Distributed Prioritized Experience Replaycited by 269
  4. 2016BOLA: Near-Optimal Bitrate Adaptation for Online Videoscited by 27
  5. 2019Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learningcited by 11

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  1. 2025SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning
  2. 2026DQ-Ladder: A Deep Reinforcement Learning-based Bitrate Ladder for Adaptive Video Streaming
  3. 2026eBandit: Kernel-Driven Reinforcement Learning for Adaptive Video Streaming
  4. 2024Imitation Learning for Adaptive Video Streaming with Future Adversarial Information Bottleneck Principle
  5. 2023Karma: Adaptive Video Streaming via Causal Sequence Modeling
  6. 2026SafeSABR: Risk-Calibrated Adaptive Bitrate Streaming over Starlink Networks ↗
  7. 2025Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
  8. 2024EVAN: Evolutional Video Streaming Adaptation via Neural Representation