TY - GEN
T1 - Motion-prediction-based wireless scheduling for multi-user panoramic video streaming
AU - Chen, Jiangong
AU - Qin, Xudong
AU - Zhu, Guangyu
AU - Ji, Bo
AU - Li, Bin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - Multi-user panoramic video streaming demands 4∼6× bandwidth of a regular video with the same resolution, which poses a significant challenge on the wireless scheduling design to achieve desired performance. On the other hand, recent studies reveal that one can effectively predict the user's Field-of-View (FoV) and thus simply deliver the corresponding portion instead of the entire scenes. Motivated by this important fact, we aim to employ autoregressive process for motion prediction and analytically characterize the user's successful viewing probability as a function of the delivered portion. Then, we consider the problem of wireless scheduling design with the goal of maximizing application-level throughput (i.e., average rate for successfully viewing the desired content) and service regularity performance (i.e., how often each user gets successful views) subject to the minimum required service rate and wireless interference constraints. As such, we incorporate users' successful viewing probabilities into our scheduling design and develop a scheduling algorithm that not only asymptotically achieves the optimal application-level throughput but also provides service regularity guarantees. Finally, we perform simulations to demonstrate the efficiency of our proposed algorithm using a real dataset of users' head motion.
AB - Multi-user panoramic video streaming demands 4∼6× bandwidth of a regular video with the same resolution, which poses a significant challenge on the wireless scheduling design to achieve desired performance. On the other hand, recent studies reveal that one can effectively predict the user's Field-of-View (FoV) and thus simply deliver the corresponding portion instead of the entire scenes. Motivated by this important fact, we aim to employ autoregressive process for motion prediction and analytically characterize the user's successful viewing probability as a function of the delivered portion. Then, we consider the problem of wireless scheduling design with the goal of maximizing application-level throughput (i.e., average rate for successfully viewing the desired content) and service regularity performance (i.e., how often each user gets successful views) subject to the minimum required service rate and wireless interference constraints. As such, we incorporate users' successful viewing probabilities into our scheduling design and develop a scheduling algorithm that not only asymptotically achieves the optimal application-level throughput but also provides service regularity guarantees. Finally, we perform simulations to demonstrate the efficiency of our proposed algorithm using a real dataset of users' head motion.
UR - http://www.scopus.com/inward/record.url?scp=85111936542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111936542&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM42981.2021.9488771
DO - 10.1109/INFOCOM42981.2021.9488771
M3 - Conference contribution
AN - SCOPUS:85111936542
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2021 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE Conference on Computer Communications, INFOCOM 2021
Y2 - 10 May 2021 through 13 May 2021
ER -