Name
Abstract | ||
|---|---|---|
Conventional streaming solutions for streaming 360-degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach. We also develop an end-to-end testbed using standards-compliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques - two for conventional adaptive video streaming and two for 360-degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/IFIPNetworking.2019.8816847 | 2019 IFIP Networking Conference (IFIP Networking) |
Keywords | DocType | ISBN |
360-degree video streaming,adaptive video streaming,MPEG-DASH,Convolutional Neural Network (CNN),Recurrent Neural Network (RNN) | Conference | 978-1-7281-3671-4 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sohee Kim Park | 1 | 1 | 1.05 |
| Arani Bhattacharya | 2 | 21 | 7.10 |
| zhibo yang | 3 | 8 | 4.31 |
| Dasari, Mallesham | 4 | 9 | 4.22 |
| Samir R. Das | 5 | 5341 | 494.55 |
| Dimitris Samaras | 6 | 1740 | 101.49 |