Name
Abstract | ||
|---|---|---|
Most content providers are interested in providing good video delivery QoE for all users, not just on average. State-of-the-art ABR algorithms like BOLA and MPC rely on parameters that are sensitive to network conditions, so may perform poorly for some users and/or videos. In this paper, we propose a technique called Oboe to auto-tune these parameters to different network conditions. Oboe pre-computes, for a given ABR algorithm, the best possible parameters for different network conditions, then dynamically adapts the parameters at run-time for the current network conditions. Using testbed experiments, we show that Oboe significantly improves BOLA, MPC, and a commercially deployed ABR. Oboe also betters a recently proposed reinforcement learning based ABR, Pensieve, by 24% on average on a composite QoE metric, in part because it is able to better specialize ABR behavior across different network states.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3230543.3230558 | SIGCOMM |
Keywords | Field | DocType |
Video delivery, Adaptive bitrate algorithms | Computer science,Video delivery,Oboe,Algorithm,Computer network,Testbed,Auto tuning,Network conditions,Reinforcement learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5567-4 | 25 | 0.89 |
References | Authors | |
37 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahaib Akhtar | 1 | 27 | 2.29 |
| Yun Seong Nam | 2 | 28 | 2.30 |
| ramesh govindan | 3 | 15430 | 2144.86 |
| Sanjay Rao | 4 | 2227 | 237.29 |
| Jessica Chen | 5 | 26 | 1.24 |
| Ethan Katz-Bassett | 6 | 1155 | 62.80 |
| Bruno F. Ribeiro | 7 | 572 | 41.35 |
| Jibin Zhan | 8 | 539 | 67.23 |
| Hui Zhang | 9 | 8856 | 1002.58 |