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An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Recently, HTTP-based video streaming traffic has continued to increase. Therefore, video service providers have been using HTTP-based adaptive streaming (HAS) technology to reduce the traffic load of the HTTP server. Accordingly, many adaptive bit rate (ABR) schemes have been proposed to provide a high quality of experience (QoE) to video service clients. In this paper, we propose a new ABR scheme using an adaptive network-based fuzzy inference system (ANFIS), which is one of the neuro-fuzzy structures. The proposed scheme learns optimal fuzzy parameters by using (1) the learning ability of ANFIS and (2) the video streaming data providing high QoE to clients. Then, the bit rate of the next segment is determined according to these trained parameters. In the simulation using NS-3, we show that the proposed scheme selects the appropriate bit rate under various wireless network conditions and provides better QoE to clients than the existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new resource allocation scheme for symbiosis of DASH clients and non-DASH clients (RASS) to provide seamless video streaming service to DASH clients and to guarantee fairness among non-DASH clients. In RASS, all the LTE downlink resource blocks are divided into two chunks by a KKT-conditions-based Resource-Division Controller (KCRDC). With the two divided chunks, the proposed scheme leads PFRA and KKT-conditions-based resource allocation scheme (KCRA) to allocate each chunk to non-DASH clients and DASH clients, respectively. PFRA allocates resource blocks with maintaining good trade-off between fairness and spectrum efficiency. On the other hand, KCRA allocates more resource blocks to DASH clients in the risk of re-buffering events to preferentially reduce the re-buffering-time ratios of the DASH clients. Finally, simulations are completed using the NS-3 (network simulator). The obtained results show that the proposed scheme shows a better synthetic performance than the existing resource allocation schemes in consideration of the re-buffering-time ratios and QoE value of DASH clients and the fairness among non-DASH clients. [ABSTRACT FROM AUTHOR]