In this section, we evaluate the proposed VCC-DASH and compare it with a classic bitrate adaptation strategy, known as LIU’s strategy [
5], under the same simulation scenarios setup. The simulation results show that the two strategies have similar performances in bitrate switching numbers, playback interruption times and buffer lengths. Moreover, the bitrate and the MOS of the segments selected by VCC-DASH are distinctly higher than that of LIU’s strategy, which means that VCC-DASH offers users better QoE.
4.1. Simulation Scenarios Setup
The proposed strategy VCC-DASH is performed on 100 video segments with different content complexities extracted from the video sequences Akiyo, Container, Foreman, Coastguard, Soccer, and Football, respectively, which are re-arranged as the VCC distribution shown in
Figure 5b. For a fast start, assuming that when there are five segments with the lowest bitrate
in the buffer, the video begins to play and the proposed strategy VCC-DASH comes into effect. The parameters settings are shown in
Table 4.
Since LIU’s strategy [
5] is one of the most famous strategies among the existing DASH bitrate adaptation ones, our simulations regard it as a benchmark to investigate the performances of VCC-DASH. In fact, both of them have the same point in adaptively matching the media representation with the network condition and pursuing goals, including less switching times, higher average bitrate, no buffer overflow or underflow, and no playback interruptions. Different from LIU’s strategy, VCC-DASH differentiates segments in content complexity and considers the constraint of buffer occupancy. In addition, the comparisons of the two strategies are done under the same scenario’s setup, including the network bandwidth, buffer size, segment duration, content complexity, available bitrate set, etc.
4.2. Simulation Results Analysis
Under a worst network condition (as shown in
Figure 6a) where bandwidth fluctuations with higher amplitudes occur frequently, our simulations compare the performances of the two strategies in terms of selected bitrate, the QoE items (including MOS and loss), and the buffered media time. If the strategy acts well in this worst condition, it can also adapt to normal conditions. In simulations, the distribution of VCC of 100 segments is shown in
Figure 6b.
(1) The selected bitrate: The selected bitrate of the two strategies is shown in
Figure 6c. The average bitrate of VCC-DASH is 492.77 Kbps, which is significantly higher than the 452.67 Kbps in LIU’s strategy. The statistical distribution of the bitrate is shown in
Figure 7a. The bitrate selected by VCC-DASH is concentrated at 540 Kbps and 720 Kbps while that of LIU’s strategy is concentrated at 360 Kbps and 540 Kbps. The reason for the results is that LIU’s strategy deploys a step-wise switching-up and aggressive switching-down method to change the media representation and prevent buffer underflow, which means it is easy for the bitrate to switch down but it is cautious to switch up, so the bitrate is more concentrated at a relative low bitrate and the average bitrate is low. The VCC-DASH directly selects the segment with the best QoE under the constrains of the network bandwidth and buffer occupancy without limit to the switching range between consecutive segments, so the network bandwidth is well used to transmitting the segment with higher bitrate and the bitrate is more concentrated at higher bitrate.
(2) QoE items: Statistics of QoE items are shown in
Table 5. For the two strategies, there is no significant difference in the total number of switching times and switching range, and furthermore, both of them are at a rather small level. Although differently, the sum of the MOS and the sum of QoE of VCC-DASH are obviously higher than that of LIU’s strategy.
Moreover, the distribution of MOS is shown in
Figure 7b, and the proportion of subjective opinion “excellent” is as high as 86% in VCC-DASH, which is significantly higher than that of LIU’s strategy. The advantage roots from that VCC-DASH collect bitrate surpluses of the prior segments and provide them to segments with high VCC so that the MOS of the segments with high VCC is visibly enhanced, and at the same time the QoE of the whole video is more equalized. In general, the proposed VCC-DASH can improve users’ QoE and offer an equalized viewing experience.
In addition to the above comparison, we add a comparative experiment under the constraint of the equal transmitting bits for any requiring segment in order to further illustrate the advantage of VCC-DASH relative to LIU’s strategy. Here according to VCC-DASH, we can obtain the bitrate selection decision (i.e., the bitrate sequence for the 100 segments under two network conditions). Then in the experiment, LIU’s strategy requests and delivers each segment based on the bitrate sequence pre-defined by VCC-DASH. Apparently, each segment received and played by LIU’s strategy has the same bit number as the corresponding segment by VCC-DASH. Under an associated VCC-DASH bitrate sequence, we obtain the QoE statistics results of LIU’s strategy as shown in
Table 6. Compared with the results in
Table 5, we observe that LIU’s QoE under the associated VCC-DASH bitrate sequence is much worse than the ones under the respective optimal bitrate sequences independently determined by their self-adaption policies. Numerically, LIU’s QoE sum of 100 segments at the associated VCC-DASH bitrate sequence (that is about 327.32 in
Table 6) is lower by 24.83% than the one at LIU’s optimal bitrate sequence (that is about 408.55 in
Table 5), and much lower at 29.06% than VCC-DASH’s QoE sum at its optimal bitrate sequence (that is about 422.44 in
Table 5). As shown by
Figure 7b and
Figure 8b in much more detail, the number of excellent-level MOS for LIU’s reduces from 74 to 31 when the bitrate selection of segment changes from adjusting by LIU’s to being predefined by VCC-DASH. Here, most of the excellent-level MOS for LIU’s degrade to the good-level (about 24) and fair-level ones (about 19). The reason for degradation of MOS levels for LIU’s roots from the mismatch of the associated VCC-DASH bitrate sequence to LIU’s decision-making solution under the network bandwidth condition is shown in
Figure 6a. Hence, the QoE performance of LIU’s is much worse than that of VCC-DASH in the case of the exact same received bits.
(3) Buffered media time: The buffer occupancy is shown in
Figure 6d where both maintain buffer occupancy in a fair proper level, and do not appear to overflow or underflow. For LIU’s strategy, the results root from two causes. The first is its step-wise switching-up and aggressive switching-down method, which avoids the buffer underflow. The second is that the client should pause a certain period of time to request the next request if the buffer occupancy is large enough to cover the maximum draining of buffered media time during fetching the segment, which prevents buffer overflow. For VCC-DASH, the results root from one cause, which is that VCC-DASH sets the upper bound
and the lower bound
for the buffer occupancy to prevent buffer overflow or underflow, respectively. As a result, the network bandwidth suddenly drops from 500 Kbps to 0 Kbps in 105 s; the buffer occupancy decreases rapidly but is still above 0, which means that although the network performs extremely bad, playback interruptions will not occur. As a whole,
Figure 6d shows that the two strategies can well control the filling level of the client buffer to avoid overflow and underflow.