Content for  TR 26.998  Word version:  18.0.0

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A.3  Use Case 18: Streaming of volumetric video for glass-type MR devicesp. 106

A.3.1  Use case descriptionp. 106

Use Case Description: Streaming volumetric video for glass-type MR devices
Bob and Patrick are gym instructors and run a gym 'VolFit'. 'VolFit' provides their clients with a mixed-reality application to choose and select different workout routines on a 5G-enabled OHMD. The workout routines are available as high-quality photorealistic volumetric videos of the different gym instructors performing the routines. Bob and Patrick book a professional capture studio for a high-quality photorealistic volumetric capture of the different workout routines for their clients. Bob and Patrick perform the workout routines in the studio capture area. The studio captures Bob and Patrick volumetrically.
Alice is a member of 'VolFit' gym. Alice owns a 5G-enabled glass-type OHMD device. The 'VolFit' MR application is installed on her OHMD. The OHMD has an untethered connection to a 5G network.
Alice wears her OHMD device. The MR application collects and maps spatial information of Alice's surrounding from the set of sensors available on the OHMD. The OHMD can further process the spatial mapping information to provide a semantic description of the Alice's surrounding.
Alice wants to learn a workout routine from her instructors, Bob and Patrick. The photorealistic volumetric videos of Alice's instructors are streamed to the MR application installed on her OHMD. The MR application allows Alice to position the volumetric representations of Bob and Patrick on real-world surfaces in her surroundings. Alice can move around with 6DoF, and view the volumetric videos from different angles. The volumetric representations are occluded by real-world objects in the XR view of Alice; when Alice move to a location where the volumetric objects are positioned behind real-world objects or vice-versa. During the workout session, Alice gets the illusion that Bob and Patrick are physically present in her surroundings, to teach her the workout routine effectively.
The MR application allows Alice to play, pause and rewind the volumetric videos. The functions can be triggered for example by hand-gestures, a dedicated controller connected to the OHMD, etc.
MR (XR5G-A1, XR5G-A2, XR5G-A4, XR5G-A5)
Degrees of Freedom:
Streaming, Split-rendering
OHMD with/without a controller
  • The application uses existing hardware capabilities on the device, including A/V decoders, rendering functionalities as well as sensors. Inside-out tracking is available.
  • Spatial mapping to provide a detailed representation of real-world surfaces around the device
  • Media is captured properly (refer to clause 4.6.7 of TR 26.928). The quality of the capture depends on different factors:
    1. Point-cloud based workflows
      • Studio setup i.e. camera lenses, distance of the captured object from the camera(s), stage lights
      • Filtering/Denoising algorithms
    2. Mesh-based workflows
      • Mesh reconstruction algorithms (e.g. Poisson surface reconstruction)
      • Geometric resolution of the object i.e. poly counts
      • Texture resolution e.g. 4K, 8K, etc.
  • Media is accessible on a server
  • Connectivity to the network is provided
Requirements and QoS/QoE Considerations
  • QoS:
    • bitrates and latencies that are sufficient to stream a high-quality volumetric content within the immersive limits
    • bitrate for a single compressed volumetric video (mesh compression using tools such as Google Draco [30] and texture compression using video encoding tools such as H.264), for example, "Boxing trainer" sequence [31] further processed to generate a 3D mesh sequence with 65,000 triangles; 25fps, Texture: 2048x2048 pixels; 25fps:
      • Data rate of 47.3Mbps, which constitutes of following:
        • Mesh sequence: 37 Mbps (using Google Draco [30])
        • Texture sequence: approximately 10 Mbps (encoding using H.264)
        • Audio: 133 kbps (AAC)
    • access link bitrate estimates in case of split-rendering delivery methods (multiple objects):
    • approximately 30% higher bitrate than typical video streaming due to ultra-low delay coding structure (e.g. IPPP)
    • left and right view (packed stereo frame)
    • bitrates of a compressed stereo video depend on rendered objects resolution
    • bitrates approximately 1Mbps (small objects)-35 Mbps (objects covering majority of the rendered viewport)
  • Required QoE-related aspects:
    • volumetric video captured roughly in the range of ~1-10 million points per frame (this is dependent on capturing workflows as well as the level of details in captured object e.g. clothes' textures)
    • high geometric resolution of the volumetric object's geometry to achieve accurate realistic simulations of rendering equations
    • frame rate at least 30 FPS and above
    • high-quality content rendering according to the user's viewpoint
    • real-time rendering of multiple high-quality volumetric objects
    • fast reaction to user's head and body movements
    • fast reaction to hand-gestures, or a connected controller, etc.
    • real-time content decoding
    • accurate spatial mapping
    • accurate tracking
    • Desired QoE-related aspects:
      • accurate scene lighting [Note: PBR feasibility]
Volumetric content production: Device Features:
  • Spatial mapping
  • Tracking
  • Scene understanding [32]
  • A/V decode resources
Selected Devices/XR Platforms supporting this: Current solutions: For a real-time mobile on-device mesh-based system, an acceptable-quality experience for 30 FPS can be achieved using at least 30,000-60,000 poly count for a volumetric object's geometry with at least 4K texture resolution. On-device rendering of multiple complex 3D models is limited by graphics capabilities of the device.
In addition, advanced rendering techniques for lighting, reflection and etc. are subject to complex rendering equations which may result in inconsistent frame rate and increased power consumption. Therefore, it is challenging to achieve a real-time volumetric streaming for multiple high-quality 3D models with current networks and on-device hardware resources. Some existing solutions use remote rendering for streaming volumetric video:
Azure remote rendering,
allows to render a huge and complex 3D model with millions of polygons remotely in cloud and stream in real-time to a MR device such as HoloLens 2TM. An intuitive demonstration of the AzureTM remote rendering of 3D model with approximately 18 million polygons on HoloLens 2TM is publicly available at:
Mesh-based multiple high-quality volumetric video streaming using remote rendering [33]. More information is available at:
Nvidia CloudXR: Scene Lighting:
The light sources included in the scene have a significant impact on the rendering results. The light sources share some common properties such as;
  • Colour: the colour of the light
  • Intensity: the brightness of the light
Under Azure remote rendering, Scene lighting [34] provides the functionality to add different light types to a scene. Only the objects in the scene with PBR material type [35] are affected by light sources. Simpler material types such Colour material [36] don't receive any kind of lighting.
Potential Standardization Status and Needs
The following aspects may require standardization work:
  • Storage and access formats
  • Network conditions that fulfill the QoS and QoE Requirements
  • Relevant rendering APIs
  • Scene composition and description
  • Architecture design

A.3.2  Call flow for STAR UEp. 109

The call flow of the establishment of the AR session for STAR UE is shown in Figure A.3.2-1:
Copy of original 3GPP image for 3GPP TS 26.998, Fig. A.3.2-1: Call flow for STAR UE
Figure A.3.2-1: Call flow for STAR UE
(⇒ copy of original 3GPP image)
A description of the steps is provided:
Step 1.
User starts the app. The app connects to the cloud to fetch a list of exercise routines for the user
Step 2.
The AP sends a list of routines to the app. Each routine is associated with an entry point for that routine. The entry point is typically a scene description that describes the objects in the scene and anchors the scene with the world space.
Step 3.
The user selects a routine in the app
Step 4.
The app fetches the scene description for the selected routine from the application provider
Step 5.
The app initializes the Scene Manager and passes the entry point to it.
Step 6.
The Scene Manager parses entry point to extract information about the required objects in the scene. In this case, the coach, the student, and a speaker are the 3 objects that will be rendered in the scene.
Step 7.
The Scene Manager informs the application about the required media that needs to be accessed.
Step 8.
The app parses the information about the media objects and determines how and when to access each of them.
Step 9.
The app informs the MSH that it will start 2 streaming sessions for the 2 dynamic objects.
Step 10.
The MSH shares the information with the AF and based on existing provisioning by the Application Provider, the AF may request QoS and charging modifications to the PDU sessions.
Step 11.
The App creates a new XR session and anchors the scene to a selected space in the XR session.
Step 12.
The media exchange begins:
  1. The static object in the scene, the loudspeaker, is fetched by the App
  2. The manifest for object 1 is retrieved
  3. The manifest for object 2 is retrieved
  4. The App configures the immersive video decoders based on the components of each object
  5. Media segment for each component of each object is fetched
  6. The segment is decoded and passed to the immersive media renderer
Step 13.
The Scene Manager periodically renders a frame by iteratively reconstructing each object and rendering it to a swapchain image. The swapchain image is passed to the AR Runtime for rendering to the HMD device.
The following media is assumed for this use case:
  • An entry point: a scene description that describes the objects in the scene.
  • Dynamic objects for the coach and student: these can be dynamic meshes, animated meshes, or point clouds.
  • Static object for the loudspeaker: this can be a static mesh.
  • Spatial audio: representing the vocal instructions associated with the dynamic object of the coach.
  • Spatial audio: representing the music for which the loudspeaker is the source.

A.3.3  Call flow for EDGAR UEp. 111

The call flow of the establishment of the AR session for EDGAR UE is shown in Figure A.3.3-1:
Copy of original 3GPP image for 3GPP TS 26.998, Fig. A.3.3-1: Call flow for EDGAR UE
Figure A.3.3-1: Call flow for EDGAR UE
(⇒ copy of original 3GPP image)
A description of the steps is provided:
Step 1.
User starts the app. The app connects to the cloud to fetch a list of exercise routines for the user
Step 2.
The AP sends a list of routines to the app. Each routine is associated with an entry point for that routine. The entry point is typically a scene description that describes the objects in the scene and anchors the scene with the world space.
Step 3.
The user selects a routine of preference in the app
Step 4.
The application sends a request for the entry point to the selected content. The Application Provider responds with an entry point to a scene description and a list of requirements for optimal processing of the scene.
Step 5.
The application determines that EDGE support is required and sends a request to the MSH to discover an appropriate Edge AS that can serve the application.
Step 6.
The MSH sends the requirements to the AF and receives a list of candidate EAS(s)
Step 7.
The MSH selects an appropriate EAS from the list of candidates.
Step 8.
The MSH provides the location of the EAS to the application.
Step 9.
The application connects to the EAS and provides initialization information. The initialization information contains: the URL to the scene description entry point or the actual scene description, its current processing capabilities, supported formats and protocols, etc.
Step 10.
The EAS configures the server application accordingly and generates a customized entry point for the client. The formats depend on the capabilities of the UE. The EAS adjusts the amount of processing performed by the EAS based on the current capabilities of the application. For example, The EAS may perform scene lighting and ray tracing and then generate a lightweight 3D scene description for the application. A less-capable UE may receive a more flattened scene, that contains stereo eye views and some depth information.
Step 11.
The App initializes the Scene Manager with the new low-complexity entry point.
Step 12.
The rest of the steps are similar to steps 6-10 from the STAR call flow.

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