ALVR - Air Light VR - Wireless Networking UPF

This fork of ALVR v20.6.0 introduces several enhancements to ALVR for monitoring and optimizing VR streaming performance.

In particular, our project integrates additional metrics to characterize the network state during streaming, providing insights into video frame (VF) delivery and network performance. Our metrics are logged in the session_log.txt file when the Log to disk setting is enabled and are also displayed in real time on the Statistics tab of the ALVR dashboard:

Our project also implements the Network-aware Step-wise ABR algorithm (NeSt-VR), an Adaptive BitRate (ABR) algorithm designed to optimize streaming quality based on real-time network conditions.

This algorithm is implemented as a new bitrate mode in ALVR, named NeSt vr:

For a comprehensive validation of several metrics and a detailed introduction and evaluation of the NeSt-VR algorithm, please refer to our paper: "Experimenting with Adaptive Bitrate Algorithms for Virtual Reality Streaming over Wi-Fi".

Added metrics overview

Note: Our client-side reported statistics —essential for obtaining the added metrics— are sent from the head-mounted diplay (HMD) to the server over TCP immediately after the client receives a VF. This feedback is received at the server significantly sooner than ALVR’s native statistics packet, which is delayed until the VF is ready for display

Time-related metrics

• Client-side frame span (frame_span_ms in GraphNetworkStatistics): time interval between the reception of the first packet to the reception of the last packet of a VF

• Frame inter-arrival time (frame_interarrival_ms in GraphNetworkStatistics): time interval between the reception of the last packet of a VF and the last packet of the previous received VF

• Video Frame Round-Trip Time (VF-RTT) (rtt_ms in GraphNetworkStatistics): time it takes for a complete VF to travel from the server to the client and for our supplementary UL packet —promptly sent upon the complete reception of the VF— to reach the server

Reliability metrics

• Packets lost (shards_lost in GraphNetworkStatistics): number of packets lost in the interval between two VF receptions

• Packets duplicated (shards_duplicated in GraphNetworkStatistics): number of packets duplicated in the interval between two VF receptions

• Frames skipped (frames_skipped in GraphNetworkStatistics): number of VFs lost prior to decoding, i.e., after network transmission due to packet losses or significant delays

• Frames dropped (frames_dropped in GraphStatistics): number of decoded VFs lost before visualization

Data rate metrics

• Instantaneous video network throughput (instant_network_throughput_bps in GraphNetworkStatistics): rate at which video data is received by the client, measured in the interval between two VFs receptions

• Peak network throughput (peak_network_throughput_bps in GraphNetworkStatistics): ratio between the VF’s size and its client-side frame span, used in NeSt-VR to estimate the network capacity

Network Stability metrics

• VF jitter (frame_jitter_ms in GraphNetworkStatistics): variation in VF time deliveries, computed as the sample standard deviation of frame inter-arrival times

• Video packet jitter (interarrival_jitter_ms in GraphNetworkStatistics): variability in video packet arrival times as defined in RFC 3550

• Filtered one-way delay gradient and One-way delay gradient (filtered_ow_delay_ms and ow_delay_ms, respectively, in GraphNetworkStatistics) : rate of change in one-way delay (OWD) between two consecutive VFs, smoothed using a Kalman filter as described in "Analysis and design of the google congestion control for web real-time communication (WebRTC)" and a state noise variance of $10^{−7}$

NeSt-VR overview

NeSt-VR applies a hierarchical decision-making process, operating every $\tau$ seconds and progressively adjusting the target bitrate ($B_v$) —initially set to ($B_0$) Mbps— in $\beta$ Mbps steps to avoid significant video quality shifts that may disrupt the user’s QoE. NeSt-VR uses the Network Frame Ratio (NFR) and VF-RTT —averaged over an $n$-sample sliding window ($\overline{;\centerdot;}$)— as inputs, adjusting the bitrate if their values surpass configurable thresholds ($\rho$ and $\sigma$, respectively). The target bitrate is also constrained within the configured maximum and minimum bitrate limits ($B_{\max}$ and $B_{\min}$) and is further upper bounded by $m \cdot C_{\text{NeSt-VR}}$ —with $m \leq 1$— to ensure the bitrate remains under our estimated network capacity ($C_{\text{NeSt-VR}}$):

Note: NFR is computed as fps_rx / fps_tx, where fps_rx denotes the average frame delivery rate and fps_tx denotes the average frame transmission rate (network_heur_fps and server_fps, respectively, in HeuristicStats)

Note: $\sigma$ is computed as $\varsigma/{\overline{\Delta_{\rm tx}}}$. $\varsigma$ is a configurable parameter and $\overline{\Delta_{\rm tx}}$ denotes the average interval between consecutive VFs transmissions

Note: $\gamma$ serves as a configurable exploration parameter to assess whether higher bitrates can be sustained and moderating the frequency of bitrate adjustments due to high VF-RTTs

Note: HeuristicStats event is logged at each NeSt-VR adjustment period, including the considered step size ($\beta$: steps_bps), the average interval between consecutive VFs transmissions ($\overline{\Delta_{\rm tx}}$: frame_interval_s), the considered ($\overline{\text{NFR}}$: network_heur_fps), the considered ($\overline{\text{VF-RTT}}$: rtt_avg_heur_s), the considered threshold for NFR ($\rho$: threshold_fps), the considered threshold for VF-RTT ($\sigma$: threshold_rtt_s), the random value drawn from a uniform distribution in the interval [0, 1] (threshold_u), and the requested target bitrate ($B_{\text{v}}$: requested_bitrate_bps)

NeSt-VR configurable parameters are outlined in the following table:

Adjustment Period	$\tau$	Step Size	$\beta$
Sliding Window Size	$n$	Estimated Capacity Scaling Factor	$m$
Minimum Bitrate	$B_{\min}$	VF-RTT Exploration Probability	$\gamma$
Maximum Bitrate	$B_{\max}$	NFR Threshold	$\rho$
Initial Bitrate	$B_{0}$	VF-RTT Threshold Scaling Factor	$\varsigma$

How to build

For detailed requirements, please refer to the ALVR GitHub repository.

To build this fork, follow the installation guide provided in the ALVR wiki, specifically for Windows operating systems: ALVR Installation Guide.

For further details on how ALVR works, consult their wiki: How ALVR Works.

Support

This project is developed with partial financial support of:

MAX-R Project (HORIZON)	Wi-XR Project (PID2021-123995NB-I00)