## 1 Introduction
### Related Work
#### 1.1.2 Experimental Studies and Wireless VR Testbeds
In this study, using a similar experimental setup as in [10; 11], the mobility patterns and orientations of the mobile device were varied. It was shown that throughput may be predicted in real time using a small neural network model with good accuracy at varying timescales. Regarding wireless VR applications, given that the prediction error was above 10% for 40% of the time at the 10 ms timescale, this work suggests to be conservative in the use of throughput prediction in this context. Off-the-shelf WiGig and WiFi interface modules were used in [13] to establish a motion-aware WiGig-WiFi interplay system for VR (MW\({}^{2}\)IVR). This testbed comprises of a local PC for processing, a Netgear Nighthawk X10 serving as access point (AP), and a Travelmate laptop with Sparrow 11 ad module. An HTC Vive headset is connected to the laptop using high definition multimedia interface (HDMI) and universal serial bus (USB) cables which allows limited translational and rotational movements in the vicinity of the laptop. The testbed collects user motion, link signal levels, and the generated frame sizes, and opportunistically switches between WiGig and WiFi while controlling the VR frame encoding for latency regulation. The distance between AP and laptop was limited in the WiGig wireless VR scenario to 3 m offering a throughput of approximately 1.5 Gbps with the antenna embedded in the laptop facing the AP (front view). However, the throughput measured for the distance of 2 m and varying antenna direction is reported to decrease almost proportionally with the antenna angle turning away from the front view. It is conjectured that this behavior may be induced by imperfect and slow beamforming operations. The throughput in the WiFi wireless VR scenario, on the other hand, was measured being constant for distances up to 5 m but dropped to one-third of the maximum throughput that was achieved by WiGig. The experimental results indicate that the MW\({}^{2}\)IVR outperforms the WiGig-only system in terms of latency regulation and video fidelity. A VR gaming testbed referred to as Air Light Virtual Reality (ALVR) is presented in [14] and used to objectively and subjectively assess the performance of cloud VR gaming. The ALVR testbed consists of a Windows workstation with an Intel Core i5 CPU, 64 GB RAM, and an NVIDIA RTX 2080 Ti GPU to constitute a cloud gaming server, a BSD workstation also with an Intel Core i5 CPU acting as traffic shaper, an ASUS RT-AC88U IEEE 802.11 ac WiFi router offering wireless connectivity, and an Oculus Quest HMD equipped with an IEEE 802.11 ac network interface. The testbed measures frame rate, latency, server load, client load, and throughput while bandwidth, delay, and packet loss rate are varied using Dummynet on the BSDF-based traffic shaper. The results indicate that bandwidth limitations may have higher influence on user experience than latency and packet loss rate. Related performance studies using cloud gaming testbeds include the work reported in [15] that adapts the open source cloud gaming platform [16] to conduct experiments on conventional mobile devices. A wireless VR platform for commodity HMDs using a programmable WiGig interface as a potential cable replacement is presented in [17]. This platform comprises of a desktop PC with Intel Core i7-4790 CPU, 16 GB memory, and an NVIDIA TITAN X CPU which runs the VR applications and renders all graphic contents. A wireless Internet protocol (IP) network was setup using the Intel WiGig modules 17265 and W13100. A Dell XPS M3800 laptop with NVIDIA GeForce 970 GPU is connected to an HTC Vive HMD for studying the computational power needed to decompress the received frames. The performance assessment of this experimental study focused on frame-processing time and end-to-end latency in the context of VR gaming. It is shown that GPU-based optimization techniques and parallel processing can reduce the total frame-processing time but it remains still challenging to meet the end-to-end latency requirements of wireless high-quality VRapplications.