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Frame interpolation for cloud-based mobile video streaming

- Usman, Muhammad, Xiangjian, He, Kin-Man, Lam, Min, Xu, Bokhari, Syed, Jinjun, Chen

  • Authors: Usman, Muhammad , Xiangjian, He , Kin-Man, Lam , Min, Xu , Bokhari, Syed , Jinjun, Chen
  • Date: 2016
  • Type: Text , Journal article
  • Relation: IEEE transactions on multimedia Vol. 18, no. 5 (2016), p. 831-839
  • Full Text: false
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  • Description: 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.

Data sharing in secure multimedia wireless sensor networks

- Usman, Muhammad, Jan, Mian, Xiangjian, He, Nanda, Priyadarsi

  • Authors: Usman, Muhammad , Jan, Mian , Xiangjian, He , Nanda, Priyadarsi
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 IEEE Trustcom/BigDataSE/ISPA;Tianjin, China; 23-26 August 2016 p. 590-597
  • Full Text: false
  • Reviewed:
  • Description: The use of Multimedia Wireless Sensor Networks (MWSNs) is becoming common nowadays with a rapid growth in communication facilities. Similar to any other WSNs, these networks face various challenges while providing security, trust and privacy for user data. Provisioning of the aforementioned services become an uphill task especially while dealing with real-time streaming data. These networks operates with resource-constrained sensor nodes for days, months and even years depending on the nature of an application. The resource-constrained nature of these networks makes it difficult for the nodes to tackle real-time data in mission-critical applications such as military surveillance, forest fire monitoring, health-care and industrial automation. For a secured MWSN, the transmission and processing of streaming data needs to be explored deeply. The conventional data authentication schemes are not suitable for MWSNs due to the limitations imposed on sensor nodes in terms of battery power, computation, available bandwidth and storage. In this paper, we propose a novel quality-driven clustering-based technique for authenticating streaming data in MWSNs. Nodes with maximum energy are selected as Cluster Heads (CHs). The CHs collect data from member nodes and forward it to the Base Station (BS), thus preventing member nodes with low energy from dying soon and increasing life span of the underlying network. The proposed approach not only authenticates the streaming data but also maintains the quality of transmitted data. The proposed data authentication scheme coupled with an Error Concealment technique provides an energy-efficient and distortion-free real-time data streaming. The proposed scheme is compared with an unsupervised resources scenario. The simulation results demonstrate better network lifetime along with 21.34 dB gain in Peak Signal-to-Noise Ratio (PSNR) of received video data streams.
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