FBDT: Forward and Backward Data Transmission Across RATs for High Quality Mobile 360-Degree Video VR Streaming

Document Type

Conference Proceeding

Publication Date

6-7-2023

Abstract

The metaverse encompasses many virtual universes and relies on streaming high-quality 360° videos to VR/AR headsets. This type of video transmission requires very high data rates to meet the desired Quality of Experience (QoE) for all clients. Simultaneous data transmission across multiple Radio Access Technologies (RATs) such as WiFi and WiGig is a key solution to meet this required capacity demand. However, existing transport layer multi-RAT traffic aggregation schemes suffer from Head-of-Line (HoL) blocking and sub-optimal traffic splitting across the RATs, particularly when there is a high fluctuation in their channel conditions. As a result, state-of-The-Art multi-path TCP (MPTCP) solutions can achieve aggregate transmission data rates that are lower than that of using only a single WiFi RAT in many practical settings, e.g., when the client is mobile. We make two key contributions to enable high quality mobile 360° video VR streaming using multiple RATs. First, we propose the design of FBDT, a novel multi-path transport layer solution that can achieve the sum of individual transmission rates across the RATs despite their system dynamics. We implemented FBDT in the Linux kernel and showed substantial improvement in transmission throughput relative to state-of-The-Art schemes, e.g, 2.5x gain in a dual-RAT scenario (WiFi and WiGig) when the VR client is mobile. Second, we formulate an optimization problem to maximize a mobile VR client's viewport quality by taking into account statistical models of how clients explore the 360° look-Around panorama and the transmission data rate of each RAT. We explore an iterative method to solve this problem and evaluate its performance through measurement-driven simulations leveraging our testbed. We show up to 12 dB increase in viewport quality when our optimization framework is employed.

Identifier

85163578874 (Scopus)

ISBN

[9798400701481]

Publication Title

Mmsys 2023 Proceedings of the 14th ACM Multimedia Systems Conference

External Full Text Location

https://doi.org/10.1145/3587819.3590987

First Page

130

Last Page

141

Grant

CCF-2031881

Fund Ref

National Science Foundation

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