# SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity

arXiv: https://arxiv.org/abs/2008.04687 · HTML: https://mlsanity.com/paper/2008.04687/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [GRACE: Loss-Resilient Real-Time Video through Neural Codecs](https://mlsanity.com/paper/2305.12333/) (2023)
- [Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception](https://mlsanity.com/paper/2106.05665/) (2021)
- [AVA: Towards Agentic Video Analytics with Vision Language Models](https://mlsanity.com/paper/2505.00254/) (2025)
- [Plasticity-Aware Mixture of Experts for Learning Under QoE Shifts in Adaptive Video Streaming](https://mlsanity.com/paper/2504.09906/) (2025)
- [Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement](https://mlsanity.com/paper/2509.19669/) (2025)
- [HiVid: LLM-Guided Video Saliency For Content-Aware VOD And Live Streaming](https://mlsanity.com/paper/2602.14214/) (2026)

## Abstract (arXiv)

This paper aims to improve video streaming by leveraging a simple observation: users are more sensitive to low quality in certain parts of a video than in others. For instance, rebuffering during key moments of a sports video (e.g., before a goal is scored) is more annoying than rebuffering during normal gameplay. Such dynamic quality sensitivity, however, is rarely captured by current approaches, which predict QoE (quality-of-experience) using one-size-fits-all heuristics that are too simplistic to understand the nuances of video content. Instead of proposing yet another heuristic, we take a different approach: we run a separate crowdsourcing experiment for each video to derive users' quality sensitivity at different parts of the video. Of course, the cost of doing this at scale can be prohibitive, but we show that careful experiment design combined with a suite of pruning techniques can make the cost negligible compared to how much content providers invest in content generation and distribution. Our ability to accurately profile time-varying user sensitivity inspires a new approach: dynamically aligning higher (lower) quality with higher (lower) sensitivity periods. We present a new video streaming system called SENSEI that incorporates dynamic quality sensitivity into existing quality adaptation algorithms. We apply SENSEI to two state-of-the-art adaptation algorithms. SENSEI can take seemingly unusual actions: e.g., lowering bitrate (or initiating a rebuffering event) even when bandwidth is sufficient so that it can maintain a higher bitrate without rebuffering when quality sensitivity becomes higher in the near future. Compared to state-of-the-art approaches, SENSEI improves QoE by 15.1% or achieves the same QoE with 26.8% less bandwidth on average.