scholarly journals Joint Source and Channel Rate Allocation over Noisy Channels in a Vehicle Tracking Multimedia Internet of Things System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2858
Author(s):  
Yixin Mei ◽  
Fan Li ◽  
Lijun He ◽  
Liejun Wang
Keyword(s):  
Internet Of Things ◽  
Video Transmission ◽  
Forward Error Correction ◽  
Transmission Rate ◽  
Video Quality ◽  
Rate Allocation ◽  
Vehicle Tracking ◽  
Total Transmission ◽  
Forward Error ◽  
Camera Nodes

As an emerging type of Internet of Things (IoT), multimedia IoT (MIoT) has been widely used in the domains of healthcare, smart buildings/homes, transportation and surveillance. In the mobile surveillance system for vehicle tracking, multiple mobile camera nodes capture and upload videos to a cloud server to track the target. Due to the random distribution and mobility of camera nodes, wireless networks are chosen for video transmission. However, the tracking precision can be decreased because of degradation of video quality caused by limited wireless transmission resources and transmission errors. In this paper, we propose a joint source and channel rate allocation scheme to optimize the performance of vehicle tracking in cloud servers. The proposed scheme considers the video content features that impact tracking precision for optimal rate allocation. To improve the reliability of data transmission and the real-time video communication, forward error correction is adopted in the application layer. Extensive experiments are conducted on videos from the Object Tracking Benchmark using the H.264/AVC standard and a kernelized correlation filter tracking scheme. The results show that the proposed scheme can allocate rates efficiently and provide high quality tracking service under the total transmission rate constraints.

Rate Allocation Algorithm with Successive Refinement in Peer-to-Peer Networks

2014 ◽  
Vol 496-500 ◽  
pp. 2200-2203
Author(s):  
Yang Su ◽  
Mi Lu
Keyword(s):  
Video Transmission ◽  
Video Quality ◽  
Rate Allocation ◽  
Allocation Algorithm ◽  
Multiple Description ◽  
Successive Refinement ◽  
Peer To Peer Networks ◽  
Peer Dynamics ◽  
Description Coding ◽  
Packet Drop

We introduce a new across-peer rate allocation algorithm with successive refinement to improve the video transmission performance in P2P networks, based on the combination of multiple description coding and network coding. Successive refinement is implemented through layered multiple description codes. The algorithm is developed to maximize the expected video quality at the receivers by partitioning video bitstream into different descriptions depending on different bandwidth conditions of each peer. Adaptive rate partition adjustment is applied to ensure the real reflection of the packet drop rate in the network. Also the granularity is changed to the scale of atomic blocks instead of stream rates in prior works. Through simulation results we show that the algorithm outperforms prior algorithms in terms of video playback quality at the peer ends, and helps the system more adjustable to the peer dynamics.

A Cross-Layer Model for Video Multicast Based TCP-Adaptive FEC over Heterogeneous Networks

2010 ◽  
pp. 1879-1895
Author(s):  
Ghaida A. AL-Suhail ◽  
Liansheng Tan ◽  
Rodney A. Kennedy
Keyword(s):  
Forward Error Correction ◽  
Video Quality ◽  
Wireless Channel ◽  
Cross Layer ◽  
Radio Link ◽  
Layer Model ◽  
Data Link ◽  
Link Layer ◽  
Adaptive Fec ◽  
Forward Error

In this article, we present a simple cross-layer model that leads to the optimal throughput of multiple users for multicasting MPEG-4 video over a heterogeneous network. For heterogeneous wired-to-wireless network, at the last wireless hop there are bit errors associated with the link-layer packets that are arising in the wireless channel, in addition of overflow packet dropping over wired links. We employ a heuristic TCP function to optimize the cross-layer model of data link and physical (radio-link) layer. An adaptive Forward-Error-Correction (FEC) scheme is applied at the byte-level as well as at the packet-level. The corresponding optimal video quality can be evaluated at each client end. The results show that a server can significantly adapt to the bandwidth and FEC codes to maximize the video quality of service (QoS) in terms of temporal scaling when a maximum network throughput for each client is reached.

Analysis of Packet-Level Forward Error Correction for Video Transmission

2011 ◽  
Author(s):  
Matteo Mazzotti ◽  
Enrico Paolini ◽  
Marco Chiani ◽  
Benjamin Gadat ◽  
Cyril Bergeron ◽  
...  
Keyword(s):  
Error Correction ◽  
Video Transmission ◽  
Forward Error Correction ◽  
Forward Error

Mobile Video Streaming Over Heterogeneous Networks

2012 ◽  
pp. 737-763
Author(s):  
Ghaida A. Al-Suhail ◽  
Martin Fleury ◽  
Salah M. Saleh Al-Majeed
Keyword(s):  
Video Streaming ◽  
Heterogeneous Network ◽  
Forward Error Correction ◽  
Video Quality ◽  
Mobile Terminal ◽  
Video Stream ◽  
Streaming Server ◽  
Wireless Ip ◽  
Channel Errors ◽  
Forward Error

All-IP networks are under development with multimedia services in mind. Video multicast is an efficient way to deliver one video simultaneously to many users over such heterogeneous wired-to-wireless networks, such as in wireless IP applications where a mobile terminal communicates with an IP server through a wired IP network in tandem with a wireless network. Unicast video streaming is also an attractive way to deliver time-shifted TV to mobile devices. This Chapter presents a simple cross-layer model that leads to the optimal throughput to multiple users for multicasting video over a heterogeneous network. An adaptive forward-error-correction scheme is applied at the byte-level as well as at the packet-level to reduce channel errors. The results show that a server can significantly adapt to the bandwidth and FEC codes to maximize the video quality of service. For unicast streaming, the Chapter presents a single negative acknowledgment scheme in which a video stream is transmitted over a heterogeneous network from a streaming server to a mobile device in a WiMAX network. The broadband streaming system is compared to several candidate solutions based on originally wired network congestion controllers. Multi-connection streaming is also investigated.

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