C. Perfecto, M. S. Elbamby, J. D. Ser and M. Bennis, "Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework," in IEEE Transactions on Communications, vol. 68, no. 4, pp. 2491-2508, April 2020, https://doi.org/10.1109/TCOMM.2020.2965527
Taming the latency in multi-user VR 360° : a QoE-aware deep learning-aided multicast framework
|Author:||Perfecto, Cristina1; Elbamby, Mohammed S.2; Del Ser, Javier1,3;|
1University of the Basque Country (UPV/EHU), Spain
2Centre for Wireless Communications (CWC), University of Oulu, Finland
3ECNALIA and with the Basque Center for Applied Mathematics (BCAM), Spain
|Online Access:||PDF Full Text (PDF, 4.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003188362
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-03-18
Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360∘ HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13% lower delays in the 99th percentile.
IEEE transactions on communications
|Pages:||2491 - 2508|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) under grant TEC2016-80090-C2-2-R (5RANVIR), in part by the INFOTECH project NOOR, by the Kvantum institute strategic project SAFARI, by the Academy of Finland projects CARMA, MISSION, SMARTER and 6Genesis Flagship (grant no. 318927).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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