A buffer-based approach to rate adaptation

By Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, Mark Watson. 2014

613 citations recorded by the source corpus.

Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we sugge...

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/faeb443d9fecfca9982d49bdfffb73eac0a4221a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Hongzi Mao
2. Mohammad Alizadeh
3. P. Levis
4. Feng Qian
5. Lusheng Ji
6. Qingyang Xiao
7. Keith Winstein
8. Ravi Netravali

In Computer Vision and Pattern Recognition

1. Feng Qian
2. Lusheng Ji
3. Qingyang Xiao
4. Keyi Zhang
5. Hongzi Mao
6. Ravi Netravali
7. Vijay Gopalakrishnan
8. Bo Han

Citations in the corpus

• Learning in situ: a randomized experiment in video streaming (2019, methodology): “We find: In our real-world setting, sophisticated algorithms based on control theory [46] or reinforcement learning [25] did not outperform simple buffer-based control [18].”
• Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devices (2018, methodology): “In non-360° video streaming, numerous rate adaptation schemes have been proposed [34, 36, 44, 59].”
• Neural Adaptive Video Streaming with Pensieve (2017, methodology): “In contrast, buffer-based approaches [19, 41] solely consider the client’s playback buffer occupancy when deciding the bitrates for future chunks.”
• Optimizing 360 video delivery over cellular networks (2016, methodology): “Extensive research has been conducted on improving the QoE of DASH video [18, 21, 13, 23, 17].”