TY - GEN
T1 - Advancing user quality of experience in 360-degree video streaming
AU - Park, Sohee
AU - Bhattacharya, Arani
AU - Yang, Zhibo
AU - Dasari, Mallesham
AU - Das, Samir R.
AU - Samaras, Dimitris
N1 - Publisher Copyright:
© 2019 IFIP.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - 360-degree video streaming
KW - adaptive video streaming
KW - Convolutional Neural Network (CNN)
KW - MPEG-DASH
KW - Recurrent Neural Network (RNN)
UR - https://www.scopus.com/pages/publications/85072810146
U2 - 10.23919/IFIPNetworking.2019.8816847
DO - 10.23919/IFIPNetworking.2019.8816847
M3 - Conference contribution
AN - SCOPUS:85072810146
T3 - 2019 IFIP Networking Conference, IFIP Networking 2019
BT - 2019 IFIP Networking Conference, IFIP Networking 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IFIP Networking Conference, IFIP Networking 2019
Y2 - 20 May 2019 through 22 May 2019
ER -