Downlink media streaming with wireless fountain coding in wireline‐cum‐WiFi networks
Bibliographic record
Abstract
ABSTRACT This paper addresses the problem of streaming packetized media data in a combined wireline/802.11 network. Since the wireless channel is normally the bottleneck for media streaming in such a network, we propose that wireless fountain coding (WFC) be used over the wireless downlink in order to efficiently utilize the wireless bandwidth and exploit the broadcast nature of the channel. Forward error correction (FEC) is also used to combat errors at the application‐layer. We analytically obtain the moment generating function (MGF) for the wireless link‐layer delay incurred by WFC. With the MGF, the expected value of this wireless link‐layer delay is found and used by the access point (AP), who has no knowledge of the buffer contents of wireless receivers, to make a coding‐based decision. We then derive the end‐to‐end packet loss/late probability based on the MGF. We develop an integrated ns‐3/EvalVid simulator to evaluate our proposed system and compare it with the traditional 802.11e scheme which is without WFC capability but equipped with application‐ and link‐layer retransmission mechanisms. Through extensive simulations of video streaming, we show that streaming with WFC is able to support more concurrent video flows compared to the traditional scheme. When the deadlines imposed on video packets are relatively stringent, streaming with WFC also shows superior performance in terms of packet loss/late probability, video distortion, and video frame delay, over the traditional scheme. Copyright © 2011 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".