Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Deadline and Priority Constrained Immersive Video Streaming Transmission Scheduling

Tongtong Feng, Qi Qi, Bo He, Jingyu Wang

TL;DR

This paper proposes a deadline and priority-constrained immersive video streaming transmission scheduling scheme that builds an accurate bandwidth prediction model that can sensitively assist scheduling decisions and demonstrates the superiority of the scheme with 12%-31% gains in quality of experience (QoE).

Abstract

Deadline-aware transmission scheduling in immersive video streaming is crucial. The objective is to guarantee that at least a certain block in multi-links is fully delivered within their deadlines, which is referred to as delivery ratio. Compared with existing models that focus on maximizing throughput and ultra-low latency, which makes bandwidth resource allocation and user satisfaction locally optimized, immersive video streaming needs to guarantee more high-priority block delivery within personalized deadlines. In this paper, we propose a deadline and priority-constrained immersive video streaming transmission scheduling scheme. It builds an accurate bandwidth prediction model that can sensitively assist scheduling decisions. It divides video streaming into various media elements and performs scheduling based on the user's personalized latency sensitivity thresholds and the media element's priority. We evaluate our scheme via trace-driven simulations. Compared with existing models, the results further demonstrate the superiority of our scheme with 12{\%}-31{\%} gains in quality of experience (QoE).

Deadline and Priority Constrained Immersive Video Streaming Transmission Scheduling

TL;DR

This paper proposes a deadline and priority-constrained immersive video streaming transmission scheduling scheme that builds an accurate bandwidth prediction model that can sensitively assist scheduling decisions and demonstrates the superiority of the scheme with 12%-31% gains in quality of experience (QoE).

Abstract

Deadline-aware transmission scheduling in immersive video streaming is crucial. The objective is to guarantee that at least a certain block in multi-links is fully delivered within their deadlines, which is referred to as delivery ratio. Compared with existing models that focus on maximizing throughput and ultra-low latency, which makes bandwidth resource allocation and user satisfaction locally optimized, immersive video streaming needs to guarantee more high-priority block delivery within personalized deadlines. In this paper, we propose a deadline and priority-constrained immersive video streaming transmission scheduling scheme. It builds an accurate bandwidth prediction model that can sensitively assist scheduling decisions. It divides video streaming into various media elements and performs scheduling based on the user's personalized latency sensitivity thresholds and the media element's priority. We evaluate our scheme via trace-driven simulations. Compared with existing models, the results further demonstrate the superiority of our scheme with 12{\%}-31{\%} gains in quality of experience (QoE).
Paper Structure (10 sections, 4 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 10 sections, 4 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Scheme design
  3. Problem Definition
  4. Bandwidth Prediction
  5. Traffic Scheduling
  6. Experiments
  7. Experimental Setup
  8. Our scheme vs. Existing models
  9. Generalization
  10. Conclusion

Figures (4)

  • Figure 1: The overview of deadline and priority constrained immersive video streaming transmission scheduling scheme.
  • Figure 2: Bandwidth prediction model.
  • Figure 3: Traffic scheduling strategy.
  • Figure 4: (a-c):Performance comparison between our scheme and existing scheduling models in 3G/HSDPA and 4G/LTE hybrid network datasets. (d-f): Performance comparison between our scheme and existing scheduling models in different video and network scenarios in terms of average QoE.