Paper: Policy Learning For Video Streaming (ICLR-2024)

Reviewer comment (Novelty):
The main weakness seems to be the lack of novelty. Of course, DQN has not been used before, however, RL approach has been used (Pensieve). The paper did not explain why their approach has a better performance compared to Pensieve. The reward structure is the same, hence, the reviewer is wondering what are the advantages.

Author response:
Thanks for your review,
1. Our method is the first one, to our knowledge, to succeed in this complex task. Pensieve failed to do so, and Puffer show it in their paper.

Reviewer comment (Novelty):
Even though the performance seems to be good, but it is not improving by a lot. While it is fine in general, but, given the fact that the methodology is not novel and the improvement is not a lot, my recommendation will be towards rejection.

Author response:
Altough it may seem the performance does not improves a lot, for network purposes this actullay means a lot - every sub QoE is hard to improve because in the streaming environment there are many types of clients and the algorithm needs to take in account every one of them to succeed in the total task. Meaning it's easy to handcraft an algorithm that optimize for one client but hard to generalize.

Reviewer comment (Experiments):
Can the authors have some comparison with Pensieve?

Author response:
Thanks for your review,
1. Our method is the first one, to our knowledge, to succeed in this complex task. Pensieve failed to do so, and Puffer show it in their paper.

Reviewer comment (Evaluation):
SSIM is not the strongest quality indicator for QoE (see question below).

Author response:
Since there are many metrics we chose to use ssim because this is the method Fugu used in their paper and to our understanding is a common method. Moreover, our method does not trained for a specific metric we just optimize a function of QoE. But we can definitely add this one too.

Reviewer comment (Evaluation):
It is not clear what was used as the quality metric for the chunk. Text says - Let Q(Ki) be the video quality at which video chunk Ki was downloaded. Video quality can be measured simply in terms of the video bitrate (as in (Mao et al., 2017)), or via more nuanced measures like the structural similarity index measure (SSIM)—a method for predicting the viewer-perceived quality of digital images and videos. What was used?

Author response:
We used the ssim method

Reviewer comment (Evaluation):
Assuming SSIM was used for perceived quality, VQM is a better QoE indicator and VMAF[1] can be used to compute QoE for chunks. Since QoE is one of key findings I would want to see a more robust metric over SSIM.

Author response:
Since there are many metrics we chose to use ssim because this is the method Fugu used in their paper and to our understanding is a common method. Moreover, our method does not trained for a specific metric we just optimize a function of QoE. But we can definitely add this one too.