Media Operations Use Case for an Augmented Reality Application on Edge Computing Infrastructure

The MOPS draft, , provides an overview of operational networking issues that pertain to Quality of Experience (QoE) in delivery of video and other high-bitrate media over the Internet. However, it does not cover the increasingly large number of applications with Augmented Reality (AR) characteristics and their requirements on ABR algorithms. Future AR applications will bring several requirements for the Internet and the mobile devices running these applications. AR applications require a real-time processing of video streams to recognize specific objects. This is then used to overlay information on the video being displayed to the user. In addition some AR applications will also require generation of new video frames to be played to the user. In order to run future applications with AR characteristics on mobile devices, computationally intensive tasks need to be offloaded to resources provided by Edge Computing. Edge Computing is an emerging paradigm where computing resources and storage are made available in close network proximity at the edge of the Internet to mobile devices and sensors , . Adaptive-Bit-Rate (ABR) algorithms currently base their policy for bit-rate selection on heuristics or models of the deployment environment that do not account for the environment's dynamic nature in use cases such as the one we present in this document. Consequently, the ABR algorithms perform sub-optimally in such deployments .

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in .

A use case that considers an application with AR systems' characteristics is now described where a group of tourists are being conducted in a tour around the historical site of the Tower of London. As they move around the site and within the historical buildings, they can watch and listen to historical scenes in 3D that are generated by the AR application and then overlaid by their AR headsets onto their real-world view. The headset then continuously updates their view as they move around. The AR application processes the scene that the walking tourist is watching in real-time and identifies objects that will be targeted for overlay of high resolution videos. It then generates high resolution 3D images of historical scenes related to the perspective of the tourist in real-time. These generated video images are then overlaid on the view of the real-world as seen by the tourist. Offloading to the remote Cloud is not feasible for applications with AR characteristics as the end-to-end delays must be within the order of a few milliseconds. In order to achieve such hard timing constraints, computationally intensive tasks can be offloaded to Edge devices.

As discussed above an AR application requires offloading of its components to resources provided by Edge Computing. These components perform tasks such as real-time generation and processing of high-quality video content that are too computationally intensive for the mobile device. In addition, such applications require high bandwidth and low jitter to provide a high QoE to the user. Another consequence of running such computationally intensive applications on AR devices such as AR glasses is the excessive heat generated by the chip-sets that are involved in the computation . Finally, the battery on such devices discharges quickly when running such applications if some processing is not off-loaded to the Edge Computing. Note that the Edge device providing the computation and storage is itself limited in such resources compared to the Cloud. So, for example, a sudden surge in demand from a large group of tourists can overwhelm that device. This will result in a degraded user experience as their AR device experiences delays in receiving the video frames. In order to deal with this problem, the client AR applications will need to use Adaptive Bit Rate (ABR) algorithms that choose bit-rates policies tailored in a fine-grained manner to the resource demands and playback the videos with appropriate QoE metrics as the user moves around with the group of tourists. Thus, once the offloaded computationally intensive processing is completed on the Edge Computing, the video is streamed to the user using an optimal ABR algorithm. This imposes the following requirements on the ABR algorithm : Dynamically changing ABR parameters: The ABR algorithm must be able to dynamically change parameters given the fat-tailed nature of network throughput. This, for example, may be accomplished by AI/ML processing on the Edge Computing on a per client or global basis. Handling conflicting QoE requirements: QoE goals often require high bit-rates, and low frequency of buffer refills. However in practice, this can lead to a conflict between those goals. For example, increasing the bit-rate might result in the need to fill up the buffer more frequently as the buffer capacity might be limited on the AR device. The ABR algorithm must be able to handle this situation. Handling side effects of deciding a specific bit rate: For example, selecting a bit rate of a particular value might result in the ABR algorithm not changing to a different rate so as to ensure a non-fluctuating bit-rate and the resultant smoothness of video quality . The ABR algorithm must be able to handle this situation.