## 5 Experimentation and Discussion
Hence, video streaming experiences a lack of network resources for the streaming of fully scalable bit-stream. In 24 Mbps, scalable video with lower frame-rates, i.e., up to 15 fps and 720p gives better PDR. However, higher quality and resolutions consume more video bit-rate and additional resources in the network for streaming. As a result, high-quality video streaming suffers a lack of network resources to provide better communication quality. The experiment is carried to study the influence of node mobility and routing overheads on video communication. The results obtained are plotted as shown in Figure 7. In a mobility scenario, the nodes keep changing the position and, hence, connectivity. This change in the node connectivity leads to re-route computation in the network, which affects the communication by dropping the video data packets. Figure 7a,b show the PDR calculation for 24 Mbps and 48 Mbps network, respectively. From the result, it is observed that the base layer is stable both with and without node mobility scenario. The bitrate of the base layers is less compared to the higher levels. However, the increased bitrate leads to more packets in the communication and fails to provide stable quality in the mobility scenario. In a non-mobility, the life-time of the calculated route leads to re-computation of the streaming path; hence, the packet drops are observed in the experiment. Figure 6: Packet Delivery Ratio (PDR) observed in Honeybee video streaming for bandwidth variations (with mobility). The experimentation is carried to study the influence of video bitrates on video streaming over a wireless network. The results are shown in Figure 8. In this experiment, all scalable video bit-streams are streamed over the network and captured the received video at the receiver side, and then PDR is calculated to analyze the streaming performances. The video sequence with more motions produces more bit-rate; hence, more packets are generated while streaming over the network. The Bosphorus video sequence is more stable compared to Jockey and Honeybee sequences. The Bosphorus video sequence has a slow-moving object in the front and very far background objects; hence, the bitrate generated by the SVC encoded is less compared to Jockey and Honeybee. In Jockey and Honeybee, objects in the foreground and background are moving frequently; therefore, the bitrate generated is higher. Figure 9 shows the PDR calculation for non-mobility wireless scenario. Figure 8: Comparison of PDR observed for Jockey, Honeybee, and Bosphorus (with mobility). Figure 7: PDR observed in Honeybee video streaming for mobility and non-mobility. From these experiments, the influence of node mobility, bandwidth, and motion levels are observed used for deriving the pre-knowledge required for ADE. As the aim of this research work was to generate the extraction points for ADE, adaptation parameters were estimated considering 80% PDR is required for decoding the bit-stream and display visual quality video. The adaptation parameters for ADE are derived as shown in Tables 3-5. These tables show the adaptation parameters to obtain better QoE in regard to PDR in a bandwidth-constrained network. Tables 3-5 are the adaptation parameters for video streaming over the wireless network. These parameters are estimated based on the PDR that is observed in the experimental evaluation process. Now, the MANE can use these Tables as a reference to decide the extraction points for an available network resource and network condition. This helps the MANE to implement the adaptation on-the-fly since the knowledge that is built is available for reference and decides the scalable levels for removal. Additionally, the adaptation parameters are estimated from the experimentation that confirms that the video received meets the quality requirements (PDR) of the video communication. The major advantage of the estimation is a reduction in the delay that is involved in the decision-making.