Multimodal Robot/Human Interaction for Assisted Living
Multimodal Robot/Human Interaction for Assisted Living
Multimodal Robot/Human Interaction for Assisted Living
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International Journal on Advances in Intelligent Systems, vol 2 no 2&3, year 2009, http://www.iariajournals.org/intelligent_systems/<br />
reduce the packet loss by switching over among the<br />
peers in case of congestion. On the other hand, the<br />
randomized approach is showing packet loss and which<br />
is due to the randomness in selecting and switching<br />
over among the peers.<br />
Packet loss can be considered the most crucial factor in<br />
video decoding and, consequently, a determining factor<br />
<strong>for</strong> video Quality of Experience (QoE). It is there<strong>for</strong>e<br />
essential to verify that the P2P overlay is optimized<br />
with this regard. Table 1 summarizes findings<br />
published in [28], correlating packet loss ratio with<br />
video streaming Quality-of-Experience (QoE), <strong>for</strong> the<br />
case of single-layer coding. It is noticeable that any<br />
value above around 14% leads to poor quality.<br />
According to this table, the proposed method shows a<br />
smooth video to the end-users.<br />
Packet loss ratio (%)<br />
14<br />
12<br />
10<br />
8<br />
6<br />
4<br />
2<br />
0<br />
Packet loss<br />
ratio [%]<br />
Proposed method Randomized method<br />
c<br />
0 5 10 15 20 25 30<br />
Time ( s )<br />
Figure11 - Packets loss ratio<br />
QoE<br />
acceptability<br />
[%]<br />
0 84 Smooth<br />
Video quality playback<br />
14 61 Brief interruptions<br />
18 41 Frequent interruptions<br />
25 31 Intolerable interruptions<br />
31 0 Stream breaks<br />
Table 1 Quality of experience acceptability<br />
thresholds<br />
Another important per<strong>for</strong>mance metric in video<br />
streaming is end-to-end latency (or delay), which is<br />
critical in the process of meeting strict playback<br />
deadlines. In turn, this has a direct impact on quality of<br />
service and quality of experience.<br />
Figure 12 shows the average end-to-end delays of both<br />
scenarios. Noticeably, our approach gives a lower<br />
average latency (order of 0.02 seconds); also, we can<br />
see that the delay is consistent, due to the fact that<br />
connections are not chosen randomly. On the other<br />
hand, delay in the randomized approach is fluctuating.<br />
This variation in delay may in turn increase packet loss<br />
with detrimental effect on QoE.<br />
Figure 13 illustrates this attribute in terms of jitter. A<br />
nearly constant jitter derives from the fact that peer<br />
handover is governed by a prioritization process (based<br />
on RTT values), which ensures that nearby nodes are<br />
chosen first. The small variation in jitter in our<br />
approach is due to the fact that we still need to <strong>for</strong>ce<br />
handover even in the case of optimal connections, to<br />
maintain a good computational load balance.<br />
AVG.E2E Delay<br />
AVG.Jitter (sec)<br />
0.06<br />
0.05<br />
0.04<br />
0.03<br />
0.02<br />
0.01<br />
0<br />
0.06<br />
0.05<br />
0.04<br />
0.03<br />
0.02<br />
0.01<br />
0<br />
Proposed method Randomized method<br />
0 5 10 15 20 25 30<br />
Time ( s )<br />
Figure12 - End-to-End Delay<br />
Proposed method Randomized method<br />
0 5 10 15 20 25 30<br />
Time ( s )<br />
Figure13 - Jitter<br />
8.3 Quality of Experience<br />
Finally, the objective and subjective quality of decoded<br />
video under the proposed and randomized methods are<br />
compared. The received packets are decoded using<br />
error concealment of H.264/AVC encoder JM15. The<br />
resulting PSNR is shown in fig. 14 comparing both the<br />
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