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Real-time interactive video streaming applications like cloud-based video games, AR, and VR require high quality video streams and extremely low end-to-end interaction delays. These requirements cause the QoE to be extremely sensitive to packet losses. Due to the inter-dependency between compressed frames, packet losses stall the video decode pipeline until the lost packets are retransmitted (resulting in stutters and higher delays), or the decoder state is reset using IDR-frames (lower video quality for given bandwidth). Prism is a hybrid predictive-reactive packet loss recovery scheme that uses a split-stream video coding technique to meet the needs of ultra-low latency video streaming applications. Prism's approach enables aggressive loss prediction, rapid loss recovery, and high video quality post-recovery, with zero overhead during normal operation - avoiding the pitfalls of existing approaches. Our evaluation on real video game footage shows that Prism reduces the penalty of using I-frames for recovery by 81%, while achieving 30% lower delay than pure retransmission-based recovery.
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This content will become publicly available on May 5, 2026
Stochastic Scanning for Unsynchronized, Low-Latency Display Without Tearing
Since their creation, displays have used the top-to-bottom raster scan. In today's interactive applications, this scan is a liability, forcing users to choose between complete frames with synchronization delay; or "torn" frames without this delay. We propose a stochastic scan that enables low-latency, unsynchronized display without tearing. We also discuss an interactive display simulator that allows us to investigate the effects of stochastic and other scans on interaction and imagery.
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- Award ID(s):
- 2008590
- PAR ID:
- 10570313
- Publisher / Repository:
- Society for Information Display Technical Digest
- Date Published:
- ISSN:
- 0094-2238
- Subject(s) / Keyword(s):
- displays, scanning, latency
- Format(s):
- Medium: X
- Location:
- San Jose, CA
- Sponsoring Org:
- National Science Foundation
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