Supporting Mobile VR in LTE Networks: How Close Are We?

In recent years, we have witnessed a boom in virtual reality (VR). 21 million wearable VR headsets are projected to be shipped in 2017, resulting in $4.9 billion revenue [3]. Among all the options, the mobile VR empowered by phones is most popular, contributing 98% of the sales [1]. Despite at early stage, it appeals to the general public with low cost (~$100) and excellent convenience (no wiring). Mobile VR aims to offer users ubiquitous and high-fidelity experiences. If achieved, users can access VR "anytime, anywhere", regardless of whether they roam or remain static. They also receive smooth, high-resolution panorama views throughout VR experience. It thus demands high bandwidth and stringent end-to-end latency to synchronize the graphical displays with the user motions. A promising approach to enabling ubiquitous mobile VR is the edge-based scheme over 4G LTE networks. As shown in Figure 1, the VR headset reports sensory user motions to edge servers through the LTE network. The edge servers accept user input and deliver the requested graphics. They thus offload computation-intensive processing tasks from the battery-powered user devices. Ubiquitous access is provided by the LTE network, the only large-scale wireless infrastructure offering universal coverage and seamless mobility. In this work [2], we examine several common perceptions, and study medium-quality mobile VR (60 frames per second and 1080p resolution) over operational LTE networks. We show that, contrary to common understandings, bandwidth tends to be not the main bottleneck for medium-quality VR. Instead, network latency poses the biggest obstacle for the mobile VR. A bulk portion of network latency does not stem from wireless data transfer, but from LTE signaling operations to facilitate wireless data delivery. These operations exhibit two categories of latency deficiency: (1) Inter-protocol incoordination, in which problematic interplays between protocols unnecessarily incur delays; (2) Single-protocol overhead, in which each protocol's signaling actions unavoidably incur delays. Our analysis, together with 8-month empirical studies over 4 US mobile carriers, looks into five common beliefs on LTE network latency under both static and mobile scenarios and shows that they are wrong. In fact, they pose as roadblocks to enable mobile VR. Our three findings are centered on three existing mechanisms for data-plane signaling, which are all well known in the literature. However, their deficiencies have not been studied from the latency perspective, particularly for delay-sensitive mobile VR applications. We further describe a new finding that incurs long latency but has not been reported in the literature. Moreover, we quantify the impact of each finding under VR traffic. We devise LTE-VR, a device-side solution to mobile VR without changing hardware or infrastructure. It adapts the signaling operations, while being standard compliant. It reactively mitigates unnecessary latency among protocols and proactively masks unavoidable latency inside each protocol. It exploits two ideas. First, it applies cross-layer design to ensure fast loss detection and recovery and minimize duplicates during handover. Second, it leverages rich side-channel info only available at the device to reduce the latency. We have prototyped LTE-VR with USRP and OpenAirInterface. Our evaluation shows that, LTE-VR reduces the frequency of frames that miss the human tolerance by 3.7× on average. It meets the delay tolerance with 95% probability, which approximates the lower bounds. It also achieves latency reduction comparable to 10× wireless bandwidth expansion. Furthermore, LTE-VR incurs marginal signaling overhead (5% more messages) and extra resource (0.1% more bandwidth and 2.3% more radio grants). We further note that our findings would carry over to the upcoming 5G. LTE-VR is as well applicable to 5G scenarios. It complements the proposed 5G radio, while provides hints for 5G signaling design.

[1]  Songwu Lu,et al.  Supporting Mobile VR in LTE Networks , 2018, Proc. ACM Meas. Anal. Comput. Syst..

[2]  Jacob R. Lorch,et al.  Matchmaking for online games and other latency-sensitive P2P systems , 2009, SIGCOMM '09.

[3]  Chunyi Peng,et al.  A Control-Plane Perspective on Reducing Data Access Latency in LTE Networks , 2017, MobiCom.

[4]  Henning Wiemann,et al.  The LTE link-layer design , 2009, IEEE Communications Magazine.

[5]  Alan Liu,et al.  A Study on the Perception of Haptics in Surgical Simulation , 2004, ISMS.

[6]  Tao Wang,et al.  Mobileinsight: extracting and analyzing cellular network information on smartphones , 2016, MobiCom.

[7]  Charles Dunn,et al.  Resolution-defined projections for virtual reality video compression , 2017, 2017 IEEE Virtual Reality (VR).

[8]  Lakshminarayanan Subramanian,et al.  Adaptive Congestion Control for Unpredictable Cellular Networks , 2015, Comput. Commun. Rev..

[9]  Robert S. Allison,et al.  Tolerance of temporal delay in virtual environments , 2001, Proceedings IEEE Virtual Reality 2001.

[10]  Bernd Girod,et al.  Content Adaptive Representations of Omnidirectional Videos for Cinematic Virtual Reality , 2015, ImmersiveME@ACM Multimedia.

[11]  Paramvir Bahl,et al.  Switchboard: a matchmaking system for multiplayer mobile games , 2011, MobiSys '11.

[12]  David Chu,et al.  FlashBack: Immersive Virtual Reality on Mobile Devices via Rendering Memoization , 2016, MobiSys.

[13]  Matti Siekkinen,et al.  Dissecting the End-to-end Latency of Interactive Mobile Video Applications , 2016, HotMobile.

[14]  Feng Qian,et al.  Optimizing 360 video delivery over cellular networks , 2016, ATC@MobiCom.

[15]  Feng Qian,et al.  Characterizing radio resource allocation for 3G networks , 2010, IMC '10.

[16]  Christian Bonnet,et al.  OpenAirInterface: A Flexible Platform for 5G Research , 2014, CCRV.

[17]  Y. Charlie Hu,et al.  Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices , 2017, IEEE Transactions on Mobile Computing.

[18]  Hari Balakrishnan,et al.  Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks , 2013, NSDI.

[19]  Kyunghan Lee,et al.  An analytical framework to characterize the efficiency and delay in a mobile data offloading system , 2014, MobiHoc '14.

[20]  Joohwan Kim,et al.  Towards foveated rendering for gaze-tracked virtual reality , 2016, ACM Trans. Graph..

[21]  Alec Wolman,et al.  Outatime: Using Speculation to Enable Low-Latency Continuous Interaction for Mobile Cloud Gaming , 2015, MobiSys.

[22]  Zhuoqing Morley Mao,et al.  Discovering fine-grained RRC state dynamics and performance impacts in cellular networks , 2014, MobiCom.

[23]  Swarun Kumar,et al.  piStream: Physical Layer Informed Adaptive Video Streaming over LTE , 2015, MobiCom.

[24]  Cheng-Hsin Hsu,et al.  Using graphics rendering contexts to enhance the real-time video coding for mobile cloud gaming , 2011, MM '11.

[25]  Bo Han,et al.  Cellular Traffic Offloading through WiFi Networks , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.

[26]  S. M. García,et al.  2014: , 2020, A Party for Lazarus.

[27]  Omid Salehi-Abari,et al.  Cutting the Cord in Virtual Reality , 2016, HotNets.

[28]  Joongheon Kim,et al.  Strategic Control of 60 GHz Millimeter-Wave High-Speed Wireless Links for Distributed Virtual Reality Platforms , 2017, Mob. Inf. Syst..

[29]  Yiqun Wu,et al.  5G: Towards energy-efficient, low-latency and high-reliable communications networks , 2014, 2014 IEEE International Conference on Communication Systems.