Bandwidth prediction schemes for defining bitrate levels in SDN-enabled adaptive streaming

Abstract

The majority of Internet video traffic today is delivered via HTTP Adaptive Streaming (HAS). Recent studies concluded that pure client-driven HAS adaptation is likely to be sub-optimal, given clients adjust quality based on local feedback. In [1], we introduced a network-assisted streaming architecture (BBGDASH) that provides bounded bitrate guidance for a video client while preserving quality control and adaptation at the client. Although BBGDASH is an efficient approach for video delivery, deploying it in a wireless network environment could result in sub-optimal decisions due to the high fluctuations. To this end, we propose in this paper an intelligent streaming archi­ tecture (denoted BBGDASH +), which leverages the power of time series forecasting to allow for an accurate and scalable network- based guidance. Further, we conduct an initial investigation of parameter settings for the forecasting algorithms in a wireless testbed. Overall, the experimental results indicate the potential of the proposed approach to improve video delivery in wireless network conditions.

Description

Journal article

Keywords

SDN, QoE, DASH, OpenFlow, Stability, Streaming architecture, Adap­tive video streaming, Network-assistance, Bounded bitrate guidance

Citation