Multidimensional Cross-Layer Bandwidth Prediction for Low-Latency Real-Time Video

doi:10.12141/j.issn.1000-565X.220705

Abstract

Abstract:

The available bandwidth of 5G networks is a pivotal factor influencing real-time video services. However, how to achieve accurate predictions in the context of low-latency real-time video services remains a difficult problem. Conventional algorithms for predicting available bandwidth typically rely on data metrics at the application layer and complete the forecast according to the packet transmission strategies. Such algorithms can lead to prediction lag in complex scenarios, thereby significantly impairing the received video quality for users. To address this concern, this study proposed a novel available bandwidth prediction algorithm based on cross-layer multi-dimensional parameters. This algorithm comprehensively integrates pertinent data metrics from the application, physical, and network layers, utilizes multiple dimensions of parameters to enhance the precision of wireless network bandwidth detection. In this paper, deep reinforcement learning was adopted as the model framework to integrate offline prediction and online prediction through cross-layer and multi-dimensional data model learning for different motion scenarios. Furthermore, network packet loss rates, image quality assessments, end-to-end delays, and other link-related factors were introduced as constraints, to realize the real-time adjustment and optimization of the prediction model during the transmission process. Experimental results conducted on a semi-physical platform show that: the prediction performance of the proposed algorithm is better than the traditional prediction method, and the fitting degree of the prediction curve and the actual curve is more than 95.8%; compared with the single-layer prediction algorithm, the packet loss rate of the proposed algorithm in walking and driving scenarios decreases by 47.3% and 30.9%, respectively, and the quality of received video is improved by 12%.

Key words: cross-layer multi-dimensional parameter, real-time video stream, 5G network, available bandwidth prediction, deep reinforcement learning

CLC Number:

TN929.5

CHEN Feng, MAO Haobin, CAI Jiling, et al.. Multidimensional Cross-Layer Bandwidth Prediction for Low-Latency Real-Time Video[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(11): 18-27.

Figures/Tables 8

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数据指标	相关系数
数据指标	步行场景	驾驶场景
Latitude	0.018	0.042
Longitude	-0.130	0.048
Speed	-0.045	-0.380
Compass	0.023	0.049
RSRP	0.470	0.450
SINR	0.470	0.470
RSRQ	-0.150	0.350
Tower_id	0.130	0.160

时延/ms	视频序列	RMSE		R²
时延/ms	视频序列	步行场景	驾驶场景	步行场景	驾驶场景
0	City	10.141	35.430	0.987	0.960
	Bridge	12.128	36.549	0.975	0.967
	Highway	13.707	39.629	0.972	0.958
	Harbour	10.389	32.512	0.994	0.962
50	City	14.774	100.899	0.852	0.831
	Bridge	14.178	92.122	0.860	0.846
	Highway	14.709	104.417	0.865	0.820
	Harbour	16.380	98.242	0.851	0.843
100	City	20.146	137.509	0.741	0.661
	Bridge	17.286	130.625	0.798	0.675
	Highway	20.210	139.996	0.752	0.654
	Harbour	19.318	131.605	0.787	0.669

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