# BOLA: Near-Optimal Bitrate Adaptation for Online Videos

arXiv: https://arxiv.org/abs/1601.06748 · HTML: https://mlsanity.com/paper/1601.06748/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2016.

## Built on by (most cited first, 27 of 27)

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- [MADRL-Based Rate Adaptation for 360deg Video Streaming with Multi-Viewpoint Prediction](https://mlsanity.com/paper/2405.07759/) (2024)
- [Demystifying Reinforcement Learning in Time-Varying Systems](https://mlsanity.com/paper/2201.05560/) (2022)
- [ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming](https://mlsanity.com/paper/2104.12507/) (2021)
- [NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming](https://mlsanity.com/paper/2107.07127/) (2021)
- [Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality](https://mlsanity.com/paper/2212.09945/) (2022)
- [Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASH](https://mlsanity.com/paper/2308.11621/) (2023)
- [Multi-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks](https://mlsanity.com/paper/2407.03426/) (2024)
- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)
- [Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming](https://mlsanity.com/paper/2505.01584/) (2025)
- [Crucible: Quantifying the Potential of Control Algorithms through LLM Agents](https://mlsanity.com/paper/2510.18491/) (2025)
- [Morphe: High-Fidelity Generative Video Streaming with Vision Foundation Model](https://mlsanity.com/paper/2602.03529/) (2026)
- [Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network](https://mlsanity.com/paper/2212.14479/) (2022)
- [Real-Time Neural Video Recovery and Enhancement on Mobile Devices](https://mlsanity.com/paper/2307.12152/) (2023)
- [Karma: Adaptive Video Streaming via Causal Sequence Modeling](https://mlsanity.com/paper/2308.10230/) (2023)
- [LL-GABR: Energy Efficient Live Video Streaming Using Reinforcement Learning](https://mlsanity.com/paper/2402.09392/) (2024)
- [Imitation Learning for Adaptive Video Streaming with Future Adversarial Information Bottleneck Principle](https://mlsanity.com/paper/2405.03692/) (2024)
- [SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2509.10486/) (2025)
- [Training-Free Adaptive 360-degree Video Streaming via Semantic Potential Fields](https://mlsanity.com/paper/2603.20999/) (2026)
- [DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems](https://mlsanity.com/paper/2604.05375/) (2026)
- [eBandit: Kernel-Driven Reinforcement Learning for Adaptive Video Streaming](https://mlsanity.com/paper/2604.08791/) (2026)
- [NeuralEmu: in situ Measurement-Driven, ML-based, High-Fidelity 5G Network Emulation](https://mlsanity.com/paper/2604.26080/) (2026)
- [SemABR: Measuring Video Semantic Fidelity with Multimodal LLMs for Adaptive Bitrate Streaming](https://mlsanity.com/paper/2609.18075/) (2026)
- [NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2610.16226/) (2026)

## Abstract (arXiv)

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. Video providers segment videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, BOLA does not require prediction of available network bandwidth. We empirically validate BOLA in a simulated network environment using a collection of network traces. We show that BOLA achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. We also implemented an updated version of BOLA that is now part of the standard reference player dash.js and is used in production by several video providers such as Akamai, BBC, CBS, and Orange.