# ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming

arXiv: https://arxiv.org/abs/2104.12507 · HTML: https://mlsanity.com/paper/2104.12507/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [BOLA: Near-Optimal Bitrate Adaptation for Online Videos](https://mlsanity.com/paper/1601.06748/) (2016)
- [Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning](https://mlsanity.com/paper/1908.02270/) (2019)
- [Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale](https://mlsanity.com/paper/1305.0510/) (2013)
- [QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.02482/) (2018)
- [A Hybrid Control Scheme for Adaptive Live Streaming](https://mlsanity.com/paper/1910.06043/) (2019)
- [QoE-Based Low-Delay Live Streaming Using Throughput Predictions](https://mlsanity.com/paper/1603.00859/) (2016)
- [FastScan: Robust Low-Complexity Rate Adaptation Algorithm for Video Streaming over HTTP](https://mlsanity.com/paper/1806.02803/) (2018)

## Built on by (most cited first, 1 of 1)

- [Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications](https://mlsanity.com/paper/2410.15846/) (2024)

## Abstract (arXiv)

Adaptive Bit Rate (ABR) decision plays a crucial role for ensuring satisfactory Quality of Experience (QoE) in video streaming applications, in which past network statistics are mainly leveraged for future network bandwidth prediction. However, most algorithms, either rules-based or learning-driven approaches, feed throughput traces or classified traces based on traditional statistics (i.e., mean/standard deviation) to drive ABR decision, leading to compromised performances in specific scenarios. Given the diverse network connections (e.g., WiFi, cellular and wired link) from time to time, this paper thus proposes to learn the ANT (a.k.a., Accurate Network Throughput) model to characterize the full spectrum of network throughput dynamics in the past for deriving the proper network condition associated with a specific cluster of network throughput segments (NTS). Each cluster of NTS is then used to generate a dedicated ABR model, by which we wish to better capture the network dynamics for diverse connections. We have integrated the ANT model with existing reinforcement learning (RL)-based ABR decision engine, where different ABR models are applied to respond to the accurate network sensing for better rate decision. Extensive experiment results show that our approach can significantly improve the user QoE by 65.5% and 31.3% respectively, compared with the state-of-the-art Pensive and Oboe, across a wide range of network scenarios.