Prior work has shown that QoE-aware resource sharing for real-time interactive video can support up to three times more simultaneous sessions at acceptable quality compared to rate-fair allocation. However, the required capabilities (QoE-targeted encoding, runtime spatial complexity estimation, and rich application-network APIs) are not yet available in commercial deployments. In this paper, we take an evolutionary approach: we design a system that delivers QoE-aware resource allocation using only capabilities that can be assembled in a lab today. We extend the utility-based allocation framework to the radio resource domain by introducing composite spatial complexity, which combines a session's video spatial complexity with its time-variant spectral efficiency into a single resource demand function. To operate with commercial real-time video streaming applications that use rate-based congestion control and lack capability to measure QoE, we use external tooling for QoE measurements. We develop an incremental reallocation algorithm with per-interval limits that encode both the congestion control algorithm's speed constraint and that spatial complexity estimates are reliable only near the current rate. The resulting prototype combines external QoE measurements with congestion-signal-based rate steering and does not require modification to commercial applications. We chart an evolution path from this prototype toward full QoE-aware resource sharing, mapping emerging standards (IETF SCONE, CAMARA, Media over QUIC) to the progressive capabilities they enable.

Mobile video calls are widely used to share information about real-world objects and environments with remote collaborators. While these calls provide valuable visual context in real time, the experience of interacting with people and moving around a space is significantly reduced when compared to co-located conversations. Recent work has demonstrated the potential of Mobile Augmented Reality applications to enable more spatial forms of collaboration across distance. To better understand the dynamics of mobile AR collaboration and how this medium compares against the status quo, we conducted a comparative structured observation study to analyze people’s perception of space and interaction with remote collaborators across mobile video calls and AR-based calls. Fourteen pairs of participants completed a spatial collaboration task using each medium. Through a mixed-methods analysis of session videos, transcripts, motion logs, post-task exercises, and interviews, we highlight how the choice of medium influences the roles and responsibilities that collaborators take on and the construction of a shared language for coordination. We discuss the importance of spatial reasoning with one’s body, how video calls help participants “be on the same page” more directly, and how AR calls enable both onsite and remote collaborators to engage with the space and each other in ways that resemble in-person interaction. Our study offers a nuanced view of the benefits and limitations of both mediums, and we conclude with a discussion of design implications for future systems that integrate mobile video and AR to better support spatial collaboration in its many forms.

Applying QoE analysis to optimize usage of cellular spectrum is of high interest to mobile network operators. A key challenge is to be able to perform QoE measurement across very different types of apps, from DASH VoD to interactive applications such as Video Conferencing and Cloud Gaming. This paper presents AMVOTS, a QoE measurement system developed by AT&T, which is flexible enough to support a large range of application types and network conditions. We also discuss using AMVOTS as part of a closed loop to prototype QoE-aware radio resource allocation.

Cloud gaming (CG) services are a key enabler of more accessible and affordable gaming experiences for users worldwide. However, to deliver these services with sufficient Quality of Experience (QoE) over heterogeneous access networks, stakeholders, including network and cloud service providers, and game developers, must first understand their specific performance requirements. For that, this study investigates QoE of PC-based CG under controlled and repeatable network conditions, emulating a range of impairments including round-trip time (RTT), packet loss (PL), random jitter (RJ), bursty jitter (BJ), and bitrate. Two representative games, Terraria and Counter-Strike 2, were tested across 15 network conditions, streamed using the Moonlight/Sunshine platform. Thirty participants rated their QoE, with orchestration and data collection managed by the AL-TRUIST tool. Results show that low bitrate (0.5Mb/s), high RTT (402ms), bursty jitter (702ms), and high RJ (std=3) significantly degrade QoE, especially in QoS-sensitive games. Additionally, we evaluate mobile-CG trained QoE models on PC-CG data and observe significant prediction errors, highlighting the importance of accounting for differences in streaming platform and context when estimating QoE. These findings provide valuable insights for stakeholders optimizing PC-CG over HANs.

The performance of remotely rendered Virtual Re-ality (VR) is sensitive to temporal disturbances in communication channels. An earlier Quality of Experience (QoE) study of tem-poral impacts in the form of frame stalls has revealed difficulties with subjective disturbance ratings while performing a task in an interactive 6-degrees-of-freedom (DOF) VR environment. This study follows up on above observation by comparing QoE ratings in the presence and absence of a task. The exploratory findings show that the task-free subjective tests yield lower ratings compared to the subjective tests with task. This indicates that the participants became more sensitive to temporal impairments in the absence of a task. Also, the positive impact of reprojection on the QoE ratings decreased in the task-free environment. The simulator sickness results for individual symptoms were on similar low levels in both settings. The total score (TS) of sickness severity was higher after than before the subjective tests with task while the difference between the TS before and after the task-free subjective tests was insignificant,

The P.NATS Phase 2 competition in ITU-T Study Group 12 resulted in both the ITU-T Rec. P.1204 series of recommendations, and also a large dataset for HTTP-based adaptive streaming QoE assessment that is now made openly available as part of this paper. The presented dataset consists of 3 subjective databases targeting overall quality assessment of a typical HTTP-based Adaptive Streaming session consisting of degradations such as quality switching, initial loading delay, and stalling events using audiovisual content ranging between 2 and 5 minutes. In addition to this, subject bias and consistency in quality assessment of such longer-duration audiovisual contents with multiple degradations are investigated using a subject behaviour model. As part of this paper, the overall test design, subjective test results, sources, encoded audiovisual contents, and a set of analysis plots are made publicly available for further research.

Remotely rendered Virtual Reality (VR) typically has to rely upon less capable and less reliable communication channels as compared to locally rendered VR. Such communication channels pose the risk of impairing the VR experience with temporary disturbances in form of frame stalls that would not be present in local VR. However, it is not obvious to which extent the durations of temporary stalls affect the Quality of Experience (QoE) in interactive 6-degrees-of-freedom (DOF) VR, and neither to which extent present reprojection/warping techniques mitigate any adverse effects of such stalls on user perception. In this paper, subjective experiments were conducted with N =29 users to quantify the relationship between QoE and single, isolated stall durations in interactive 6-DOF remote VR when stalls either freeze the frame in place, or when frames are reprojected by the VR driver. Results indicate that given a 90 fps headset and a simple task that requires moderate head-movement; (1) short, isolated stalls of up to 4 frames go unnoticed on average, no matter whether reprojection is used or not, (2) the number of perceived stalls when using reprojection is the same or lower than the reference (no stall) in the range 1–4 frame stalls, (3) reprojection entails significantly higher user ratings for stalls ranging from 8 frames and beyond, (4) the improvement of using reprojection over freezes in terms of making more stalling events imperceptible is highest in the range 8–16 frames.

A dataset has been created from an experiment regarding the subjective Quality of Experience (QoE) in relation to stalling video feeds in interactive 6-Degrees-of-freedom Virtual Reality (6-DOF VR). The main purpose of collecting this data is to provide insight into the QoE of stalls that may occur in interactive remote-rendered 6-DOF VR. The data contains information regarding user ratings coupled with various stall lengths and for both frozen or reprojected images. Additionally, user motion and pixel changes (in the form of Mean Square Errors) in successive images in the vicinity of the stall are also stored. In summary, 29 users participated in testing, each rating 16 scenes, amounting to a total of 464 rated scenes.

Rendering VR-content generally requires large image resolutions. This is both due to the display being positioned close to the eyes of the user and to the super-sampling typically used in VR. Due to the requirements of low latency and large resolutions in VR, remote rendering can be difficult to support at sufficient speeds in this medium.In this paper, we propose a method that can reduce the required resolution of non-panoramic VR images from a codec perspective. Because VR images are viewed close-up from within a headset with specific lenses, there are regions of the images that will remain unseen by the user. This unseen area is referred to as the Hidden-Area Mesh (HAM) and makes up 19% of the screen on the HTC Vive VR headset as one example. By remapping the image in a specific manner, we can cut out the HAM, reduce the resolution by the size of the mesh and thus reduce the amount of data that needs to be processed by encoder and decoder. Results from a prototype remote renderer show that by using the proposed Hidden-Area Mesh Remapping (HAMR), an implementation-dependent speed-up of 10-13% in encoding, 17-18% in decoding and 7-11% in total can be achieved while the negative impact on objective image quality in terms of SSIM and VMAF remains small.

This paper shows the impact of bitrate settings on objective quality measures when streaming non-panoramic remote-rendered Virtual Reality (VR) images. Non-panoramic here refers to the images that are rendered and sent across the network, they only cover the viewport of each eye, respectively.