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Cross-layer Optimized Video Streaming over Wireless Multi-hop Mesh Networks 121 in .NET Print qrcode in .NET Cross-layer Optimized Video Streaming over Wireless Multi-hop Mesh Networks 121

Cross-layer Optimized Video Streaming over Wireless Multi-hop Mesh Networks 121 using barcode integrated for .net control to generate, create qr-codes image in .net applications. Web app 5.6 Experimental Results We experimented with a variet y of typical video content (Common-Interchange Format CIF sequences Foreman , Silent , Hall Monitor , and Stefan , each consisting of 300 frames with 30 Hz replay rate). We used a fully-scalable codec [24] and the produced bitstream was extracted at an average bitrate of 2 Mbps and packetized into MSDUs of data payload not larger than 1000 bytes. The end-to-end delay for the MSDUs of each GOP was set to 0.

54 sec, which corresponds to the replay duration of one GOP. We remark that although the utilized video coder is not a member of the MPEG family of coders, the assumptions made in Subsection 5.2.

3 for the distortion-reduction estimation and the application-layer packet scheduling are also valid for the scalable coder currently standardized by the JSVM group of MPEG/VCEG [25] since it is based on open-loop motion-compensated prediction and update steps followed by embedded quantization and context-based entropy coding. Hence our methods and experiments are relevant to future systems that will utilize such scalable video coding technology in the context of mesh networks. We simulated the cases of the multi-hop mesh network topologies of Figure 5.

2, labeled T1-T3, under predetermined transmission intervals for each link. Our simulation took into account the different parameters for the various layers, such as varying SINR, transmission overheads at the MAC layer due to MSDU acknowledgements and polling overheads, as well as queuing and propagation delays in the various links of the mesh network. In order to incorporate the effect of noise and interference, we performed a number of simulations using random values for the SINR of each link, chosen between 15 and 25 dB.

Network feedback via the overlay network was conveyed to each hop whenever a significant change in the experienced channel condition occurred. For the end-to-end optimization with network feedback (termed End-to-end in our results) this includes the information conveyed from all hops. However, we also considered a localized case where the information horizon was set to the direct neighborhood of each hop (termed Localized in our results this information horizon is shown pictorially in Figure 5.

2) and the remaining network parameters were estimated as explained in Subsection 5.4.2.

In addition, a purely estimation-based case was also considered with no horizon , where the only available information is the channel SINR range (smin, smax of (28)) for each link, communicated by the overlay network infrastructure whenever the channel variation exceeded 2 dB (termed Estimation based ) from the estimated value given by (28). This ensured that the information cost for the dissemination of the network information is minimal compared to the other alternatives, as indicated in Table 5.1.

Notice that, both for the Localized case, as and the Estimation based case, the theoretical framework of (18) (28) was used. Apart from the various alternatives of the proposed optimization, we also derive results with streaming under two other optimization algorithms. The first case is optimization based on the expected transmission count (ETX) [8], where the utility function is chosen such that the retransmission limit of each MSDU is set based on the effective network bandwidth and the expected error rate.

This case considers the MSDU delay deadline from a purely network-centric approach [8], i.e., it does not use the.

122 Cross-layer Optimized Video Streaming over Wireless Multi-hop Mesh Networks constraints set in (15), (16) , but rather restricts the MSDU delay deadline based on link loss ratios and the available throughput [8]. It was termed ETX optimized in our results. Secondly, the case of selecting the link with the highest effective bandwidth was realized for the routing of each MSDU, since it corresponds to the popular solution for optimized routing [26] (termed as the Highest Bandwidth solution).

Notice that, in both cases, the best modulation was established as in the end-to-end case, and each link s status information was also used for these cases, as conveyed by the overlay network infrastructure. As a result, the differences in performance stem purely from the different performance utilities that were chosen during the MSDU routing and path selection. Effectively, this separates the fully network-aware methods (proposed End-to-End , Highest Bandwidth [26], and ETX optimized [8]) from the partial network-aware approaches (proposed Localized and Estimation based ).

In addition, within the fully network-aware methods, the difference in the performance utilities means that only the End-to-End approach fully utilizes application-layer, MAC, and PHY parameters via the optimization framework of (12)-(16). Indicative results for the obtained average PSNR of each method are given in Table 5.2 (25 runs per sequence/method/topology).

Two representative cases of medium and low average transmission bandwidth were chosen.. Table 5.2: Average PSNR resul QR Code 2d barcode for .NET ts (Y-channel 25 runs with 300 video frames per run) for video streaming in the multi-hop networks of Figure 5.

2. Medium bandwidth case PSNR (dB) Low bandwidth case PSNR (dB) Method/Topology T1 T2 T3 T1 T2 T3 End-to-end 35.42 34.

28 32.89 32.11 30.

56 31.54 ETX optimization 34.15 31.

89 31.58 30.33 29.

74 29.55 Highest Bandwidth 33.18 30.

51 30.45 28.66 27.

22 27.00 Localized 34.08 32.

48 30.86 29.67 28.

55 28.19 Estimation based 33.21 30.

11 29.81 29.31 27.

45 27.12. In order to understand better the relationship between the obtained PLR for each case and the derived PSNR, the percentage of losses for the video packets when clustered into eight distinct distortion categories is presented in Figure 5 (example for the sequence Foreman ). The second topology of Figure 5.2 was used for these results; similar results have been obtained for the remaining topologies and the remaining video sequences.

Notice that our choice of eight distinct categories is only performed for illustration purposes, since each packet is associated with its own distortion-reduction. In our simulations, the packet losses were mainly due to deadline violation, since each hop drops the packets which have already expired. The results of Figure 5 indicate, for all the scenarios under consideration, that scheduling at the application layer by expected distortion-reduction leads to reduced losses for the most significant classes of packets.

This justifies our use of a scalable video coder that permits such a scheduling. However, each method achieves different PSNR performance and PLRs depending on its chosen utility and the presence of network feedback. As shown in the results of Table 5.

2, the End-to-end case outperforms all other methods by a significant margin. The ETX optimization appears to perform relatively well, even though it is outperformed by approximately 1.5 dB by the End-to-end case for.

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