Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation

TL;DR

This paper introduces BE-ABR, a bandwidth-efficient bitrate adaptation algorithm that significantly reduces traffic wastage in video streaming while maintaining high QoE, by modeling buffered data volume and predicting transmission delays.

Contribution

The paper rigorously formulates buffered data volume dynamics and proposes a novel, time-aware, Transformer-based delay prediction model for improved bitrate adaptation.

Findings

Achieves 60.87% reduction in traffic wastage.

Maintains or improves QoE compared to existing methods.

Effective across WiFi, 4G, and 5G networks.

Abstract

Bitrate adaptation (also known as ABR) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABR algorithms suffer from severe traffic wastage, which refers to the traffic cost of downloading the video segments that users do not finally consume, for example, due to early departure or video skipping. In this paper, we carefully formulate the dynamics of buffered data volume (BDV), a strongly correlated indicator of traffic wastage, which, to the best of our knowledge, is the first time to rigorously clarify the effect of downloading plans on potential wastage. To reduce wastage while keeping a high QoE, we present a bandwidth-efficient bitrate adaptation algorithm (named BE-ABR), achieving consistently low BDV without distinct QoE losses. Specifically, we design a precise, time-aware transmission delay prediction model over the Transformer architecture, and develop a fine-grained buffer control scheme. Through extensive experiments conducted on emulated and real network environments including WiFi, 4G, and 5G, we demonstrate that BE-ABR performs well in both QoE and bandwidth savings, enabling a 60.87\% wastage reduction and a comparable, or even better, QoE, compared to the state-of-the-art methods.