Al-Issa, Ali EdanBentaleb, AbdelhakBarakabitze, Alcardo AlexZinner, ThomasGhita, Bogdan2023-03-222023-03-222019http://www.suaire.sua.ac.tz/handle/123456789/5076Journal articleThe 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.enSDNQoEDASHOpenFlowStabilityStreaming architectureAdap­tive video streamingNetwork-assistanceBounded bitrate guidanceArticle