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The likely reason is un acknowledged packets. TCP/IP requires that every packet be acknowleged, but it doesn't require it to happen immediately. At very high transmission rates such as FIOS provides, any delay in ackowledging a packet or delay in re-transmitting a packet may cause the router memory to fill up with subsequent packets, creating the proverbial Gordian knot. You cannot get rid of packets in memory because they haven't been acknowledged, but you cannot get any more acknowledgements, because there is no memory left in the router to receive them! I suspect this is a situation the router doesn't handle gracefully.
If it does, then you have a stressed line problem that only occurs when the line is close to saturated, which is a common problem with lines that are marginal, and would probably require a trouble ticket.
You may also have a issue with fios itself, because you are streaming video you can see it where as if your just going to websites you will not see a network hiccup. I stream netflix all the time with no issues. I highly doubt it is a issue of the router running out of memory, because your router does not store what you are downloading, it is just routing on the packets of info onto the correct mac address that requested the info from the website., it does not buffer them.
I still think it is the router running out of memory. Contrary to what others have said, the router does have to store packets. It is just like any other node on a TCP/IP network. It has to hold onto the packet until the next node up the line has ack'ed sucessful receipt. TCP/IP does require that every sent packet be acknowledge, but to deal effectively with high speed transmission, and long propogation time, it can send the next packet, in fact it can keep on sending packets until the packet is ack'd. The packets do have to be ack'd in order, but It can take a long time. The obvious problem is at very high transmission rates, delays in ack of even due to just propagation delay can chew up a lot of memory in the router. The faster you make the connection, the faster the router will run out of memory, so making the connection faster will make matters worse.
While it is configured that way, some other things may not work, but if the netflix streaming video works without the router, there is your answer. The PC has far more memory available for packets than the router. so is far more tolerant of long delayed acks.
I'm having same issue, waited my 30 minutes with Netflix support and that said "no idea" and that they are working on buffering issue. Has anyone got past this yet? Very frustrating and wonder what is up w/router to just drop like that?
how did you access the ONT outside? all locked up? but after doing so you ran cat 5 cable for that to the inside to a wireless router and use that for internet and leave the actiontec in place for the TV side? did you disable anything in actiontec?
In terms of getting this done with Verizon Tech Support, we need more detail, they are stingy with helping anymore. what number did you call and what was the wording you used? i'm guessing they will say not suppoted so get lost.
Even before millions were confined to their homes by a global pandemic, improvements in internet connections and service offerings had led to an exponential increase in the use of streaming video around the world. With few options left for entertainment, streaming services are taking off. In this commentary, we examine the carbon footprint of these services.
Streaming services are associated with energy use and carbon emissions from devices, network infrastructure and data centres. Yet, contrary to a slew of recent misleading media coverage, the climate impacts of streaming video remain relatively modest, particularly compared to other activities and sectors.
Drawing on our analysis and other credible sources, we expose the flawed assumptions in one widely reported estimate of the emissions from watching 30 minutes of Netflix. These exaggerate the actual climate impact by up 90 times.
The relatively low climate impact of streaming video today is thanks to rapid improvements in the energy efficiency of data centres, networks and devices. But slowing efficiency gains, rebound effects and new demands from emerging technologies, including artificial intelligence (AI) and blockchain, raise increasing concerns about the overall environmental impacts of the sector over the coming decades.
Update 11/12/2020: The energy intensity figures for data centres and data transmission networks were updated to reflect more recent data and research. As a result, the central IEA estimate for one hour of streaming video in 2019 is now 36gCO2, down from 82gCO2 in the original analysis published in February 2020. The updated charts and comparisons also include the corrected values published by The Shift Project in June 2020, as well as other recent estimates quoted by the media.
Looking at electricity consumption alone, the original Shift Project figures imply that one hour of Netflix consumes 6.1 kilowatt hours (kWh) of electricity. This is enough to drive a Tesla Model S more than 30km, power an LED lightbulb constantly for a month, or boil a kettle once a day for nearly three months. The corrected figures imply that one hour of Netflix consumes 0.8 kWh.
With 167 million Netflix subscribers watching an average of two hours per day, the corrected Shift Project figures imply that Netflix streaming consumes around 94 terawatt hours (TWh) per year, which is 200 times larger than figures reported by Netflix (0.45TWh in 2019).
The assumptions behind the Shift Project analysis (largely based on a 2015 paper, whose assumptions have been significantly revised in 2019 and 2020) contain a series of flaws, which, taken together, seriously exaggerate the electricity consumed by streaming video.
This difference stemmed from a stated assumption of 3Mbps apparently being converted in error to 3 megabytes per second, MBps, with each byte equivalent to eight bits. The Shift Project corrected this error in their June 2020 update, but did not revise any of their other assumptions, discussed below.
The Shift Project analysis overestimates the energy intensity of data centres and content delivery networks (CDNs) that serve streaming video to consumers by around 35-fold, relative to figures derived from 2019 Netflix electricity consumption data and subscriber usage data.
My original February 2020 analysis showed that the Shift Project assumptions for data transmission energy intensity (0.15-0.88 kWh/GB) were much higher than more recent estimates (0.025-0.23kWh/GB). However, the latest research shows that these data-based intensity values (kWh/GB) are not appropriate for estimating the network energy use of high bitrate applications such as streaming video. Instead, experts advise using time-based energy intensity values (kWh per viewing hour). Therefore, my assumptions for data transmission energy use have been updated with time-based energy intensity values.
Taken together, my updated analysis suggests that streaming a Netflix video in 2019 typically consumed around 0.077 kWh of electricity per hour, some 80-times less than the original estimate by the Shift Project (6.1 kWh) and 10-times less than the corrected estimated (0.78 kWh), as shown in the chart, below left. The results are highly sensitive to the choice of viewing device, type of network connection and resolution, as shown in the chart, below right.
The IEA estimate is also substantially lower than other estimates quoted in the media, including 22-times lower than the Despacito claim (cited on Channel 4, the BBC, Fortune, and Al Jazeera, assuming a global average grid mix) and 11-times lower than the claim by Save On Energy that 80 million views of Birdbox emitted 66ktCO2 (cited in the New Yorker, Euronews, Forbes, Die Welt, and the Daily Mail). My estimate of 36gCO2 per hour is over 2100-times lower than Marks et al. (2020) who estimated that 35 hours of HD video emits 2.68tCO2, or 77kgCO2 per hour.
But as the chart above shows, this figure depends heavily on the generation mix of the country in question. In France, where around 90% of electricity comes from low-carbon sources, the emissions would be around 2gCO2e, equivalent to 10 metres of driving.
Using country average emission factors may still overestimate emissions, particularly from data centres. Technology firms operating large data centres are leaders in corporate procurement of clean energy, accounting for about half of renewable power purchase agreements in recent years.
The electricity mix is also rapidly decarbonising in many parts of the world. For instance, the emissions intensity of electricity in the UK fell by nearly 60% between 2008 and 2018. Compared to 2019 levels, global emissions intensity of electricity falls by around a quarter by 2030 in the IEA Stated Policies Scenario and by half in the Sustainable Development Scenario.
As well as changes that are invisible to the consumer, there are also obvious trends in the technology seen everyday. Devices are also becoming smaller and more efficient, for example, in shifts from CRT to LCD screens, and from personal computers to tablets and smartphones.
Many new video streaming and cloud gaming services have also launched in recent months. Particularly noteworthy is the rapid growth in video traffic over mobile networks, which is growing at 55% per year. Phones and tablets already account for more than 70% of the billion hours of YouTube streamed every day.
The ease of accessing streaming media is leading to a large rebound effect, with overall streaming video consumption rising rapidly. But the complexity of direct and indirect effects of digital services, such as streaming video, e-books, and online shopping, make it immensely challenging to quantify the net environmental impacts, relative to alternative forms of consumption.
Moreover, emerging digital technologies, such as machine learning, blockchain, 5G, and virtual reality, are likely to further accelerate demand for data centre and network services. Researchers have started to study the potential energy and emissions impacts of these technologies, including blockchain and machine learning.
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