Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61398
Title: Frame interpolation for cloud-based mobile video streaming
Authors: Usman, M
He, X
Lam, KM 
Xu, M
Bokhari, SMM
Chen, J
Keywords: Block Matching Algorithm (BMA)
Error Concealment (EC)
H.265/HEVC
High Definition (HD)
Intraframe
Motion Vectors (MVs)
Packet loss rate
Packet retransmission
Quality of Experience (QoE)
Quantization Parameters (QPs)
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on multimedia, 2016, v. 18, no. 5, 7423765, p. 831-839 How to cite?
Journal: IEEE transactions on multimedia 
Abstract: Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.
URI: http://hdl.handle.net/10397/61398
ISSN: 1520-9210
EISSN: 1941-0077
DOI: 10.1109/TMM.2016.2537200
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