A Spatio-Temporal based Frame Indexing Algorithm for QoS Improvement in Live Low-Motion Video Streaming

TL;DR

This paper introduces a spatio-temporal frame indexing algorithm that reduces redundancy in live low-motion video streaming, improving bandwidth efficiency and QoS, with some tradeoffs in frame processing time.

Contribution

The paper presents a novel spatio-temporal frame indexing method that enhances video compression and QoS in live streaming by eliminating redundancy across frames.

Findings

Achieved 5-15.8% improvements in buffer size and compression ratio.

Outperformed standard and local frame indexing schemes in key metrics.

Tradeoff observed in frame build time, increased by 8.71-10.8%.

Abstract

Real-time video life streaming of events over a network continued to gain more popularity among the populace. However, there is need to ensure the judicious utilization of allocated bandwidth without compromising the Quality of Service (QoS) of the system. In this regard, this paper presents an approach based on spatio-temporal frame indexing that detects and eliminate redundancy within and across captured frame, prior transmission from the server to clients. The standard and local low motion videos were the two scenarios considered in evaluating the performance of the proposed algorithm. Results obtained showed that the proposed approach achieved an improvement of 5.13%, 15.8% and 5%, 15.6% improvement in terms of the buffer size and compression ratio. Though with a tradeoff of the frame-built time, where both the standard and local frame indexing outperforms the proposed scheme with 10.8% and 8.71% respectively.