Undeniably, RLNC method has been found robust to assist reliable data transmission over wireless network; however realizing the multimedia data transmission, particularly over LTE/LTE-A networks, assessing and enhancing it to meet standards is inevitable.
Unlike classical NC schemes where NC is applied per packet manner, our proposed S-RLNC method employs RLNC for groups of packets called generations that undeniably augments the usability of NC scheme for real-time applications.
Recently, to effectively reduce the energy consumption by the data transferred, the data communication schemes joint the CS and RLNC are discussed in [14-18].
In the proposed compressed network coding method, CS and RLNC are jointed to reduce the numbers of the transmissions and receptions based on the compressibility of the sensed data.
Based on the network model, they proposed a deterministic algorithm to encode the files, while the network model in our system is based on RLNC.
When an intermediate device receives some or all of the data slices, it could reencode the received data with RLNC and then spread the data to its neighbors via D2D communication.
One of the key differences between the deterministic linear network coding technique (DLNC) and the RLNC is that the RLNC coding coefficients increase the probability of their linear independence.
This is one of the major reasons why RLNC is preferred for network coding within WSNs as these are usually limited in terms of storage capacity .
* Right Leg Non-Touch Open eyes (RLNO) - Right Leg Non-Touch Closed eyes (RLNC
Typically, the exact decoding probability under RLNC
is derived in a fixed topology -.
et al extended the LNC and proposed the novel idea of Random Linear Network Coding (RLNC
can achieve a reasonably high successful decoding probability with a relatively small field size.