# Performance Analysis of Reliable Video Streaming with Strict Playout   Deadline in Multi-Hop Wireless Networks

**Authors:** Hussein Al-Zubaidy, Viktoria Fodor, Gy\"orgy D\'an, Markus Flierl

arXiv: 1704.02790 · 2017-04-11

## TL;DR

This paper presents a low-complexity, model-based rate adaptation algorithm for scalable video streaming over wireless networks, effectively balancing high quality and low delay in multi-hop scenarios.

## Contribution

It introduces a novel stochastic network calculus-based performance model and demonstrates its effectiveness for near-optimal rate adaptation and routing in mesh networks.

## Key findings

- Achieves less than 10% quality degradation compared to optimal performance.
- Enables fast, near optimal rate adaptation for fixed paths.
- Supports cross-layer routing and video rate adaptation in mesh networks.

## Abstract

Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high quality and low delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity, model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the model using extensive simulations. We show that it allows fast, near optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than $10$\% quality degradation compared to the best achievable performance.

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/1704.02790/full.md

## References

39 references — full list in the complete paper: https://tomesphere.com/paper/1704.02790/full.md

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Source: https://tomesphere.com/paper/1704.02790